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Global Positioning System (GPS)/Inertial Navigation System (INS) integration is a widely used technique for navigation and positioning applications. It combines the advantages of GPS and INS to provide accurate and reliable information. However, the GPS/INS integration suffers from performance degradation during a GPS outage, which occurs when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized LSTM (IncRLSTM) learning. Incremental learning is a learning paradigm that can be learned from streaming data online and updating the model parameters without forgetting the previous ones. Also, regularization is a technique that prevents overfitting and improves the generalization of the network by adding some constraints or penalties to the model. IncRLSTM learning models the GPS signal as a multi-objective regression and corrects the INS output with the Faded Memory Kalman filter. The results on real-world datasets significantly show the reduction of positioning errors by an average of 72 % during GPS outages and the improvement of the accuracy and robustness of GPS/INS integration by an average of 58 % compared with existing methods. Moreover, IncRLSTM presents, on average, a 10 % improvement compared to the existing methods. [ABSTRACT FROM AUTHOR]

In recent years, research on unmanned surface vessels has entered a new phase, demanding the increased accuracy and reliability of integrated navigation and positioning systems. However, in complex environments, due to intermittent sensor failures or communication limitations, Global Navigation Satellite System (GNSS) signals are unreliable. Various errors that emerge in the system make it impossible to complete the measurement update process of filtering algorithms. This study provides a mechanism for improving the accuracy of combined GNSS/Shipborne Inertial Navigation System (SINS) integrated navigation when a GNSS outage occurs. Firstly, a SINS denoising method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and an improved wavelet threshold algorithm is proposed. Secondly, a method based on Long Short-Term Memory (LSTM) network assisted GNSS/SINS integrated navigation is proposed to address the issue of poor information fusion performance when GNSS outages occur. The trained LSTM model is combined with SINS data to predict velocity and position during GNSS outages, providing high-precision velocity and position information. This ensures continuous, high-precision, navigation of unmanned surface vessels. Finally, to confirm the validity of the proposed methodology, a sea trial is performed to compare the different methods, and the results reveal the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository. [ABSTRACT FROM AUTHOR]

AbstractThe safety of Unmanned Surface Vehicle during the autonomous phase of navigation relies heavily on the onboard navigation equipment. However, in the face of changing and diverse navigation environments, global satellite navigation systems utilizing radio communication often face signal occlusions. This significantly reduces the positioning accuracy of navigation systems. To ensure the navigation accuracy of vehicles in various harsh environments, a Global Navigation Satellite Systems and Inertial Navigation System (GNSS/INS) integrated navigation system structure based on cascaded vector-tracking loop is proposed in this paper. The integrated navigation scheme not only improved the positioning accuracy of the navigation system but also effectively improved the robustness of the system. Compared with the traditional scalar-tracking loop and vector-tracking loop methods, it exhibits better performance. The experimental results show that the proposed method can reduce the horizontal position positioning error by 73.7% compared with the traditional vector-tracking loop method in the case of signal occlusion. Simultaneously, it can continuously output reliable and high-precision positioning information in the case of serious signal occlusion. The integrated navigation scheme proposed in this paper can be used as a research direction for the development of Unmanned Surface Vehicle navigation applications and provides solutions for the development of related navigation systems. [ABSTRACT FROM AUTHOR]

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system. [ABSTRACT FROM AUTHOR]

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a "MV + ARE + SHOE" three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology. [ABSTRACT FROM AUTHOR]

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments. [ABSTRACT FROM AUTHOR]

Launch vehicle applications require highly accurate and reliable navigation systems that can withstand the high dynamic conditions of the launch and flight phases. GNSS-aided INS (Inertial Navigation System) is the most popular solution for launch vehicle navigation. However, the accuracy of Global Navigation Satellite System (GNSS) receivers can be compromised by malicious spoofing attacks, which can result in significant safety risks and mission failure. This paper presents a method for identifying and mitigating the spoofing attacks on Global Navigation Satellite System (GNSS) receivers using the independent and simultaneous measurements of the Inertial Navigation System (INS). A state space approach is attempted in this paper with an extended Kalman Filter for identifying the spoofed signals. The well-established method of integrating INS and GNSS data for navigation accuracy improvement is taken as the baseline of this algorithm. The proposed method is an extension of a loosely coupled integration algorithm already established for fusing INS and GNSS data. The extended Kalman filter estimated position and velocity of the receiver is used along with the satellite position and velocity computed from ephemeris to find out the range and range rate of each of the satellites to the receiver. The difference between the estimated range and range rate against the measured range and range rate is considered as filter residual. The filter residuals are taken as the parameter to identify the spoofing. The threshold of filter residuals for range and range rates is the critical parameter that decides the false isolation and missing detection. The threshold is decided based on the environment in which the receiver is operated, the dynamics expected, and the noise model. The algorithms envelopment and simulation studies are done. The simulation tests are completed using a GNSS constellation simulator with spoofed signals. The INS and GNSS error model, states selection, Kalman filter design, algorithm details, simulation tests, simulation results, advantages and disadvantages of the new algorithm, and future work are covered in this paper. [ABSTRACT FROM AUTHOR]

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods. [ABSTRACT FROM AUTHOR]

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments. [ABSTRACT FROM AUTHOR]

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP's fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS. [ABSTRACT FROM AUTHOR]

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss. [ABSTRACT FROM AUTHOR]

This paper develops an asynchronous measurement processing technique for sequential filtering that effectively handles small errors in the measurement sampling epoch within a linearized framework. The derived method relaxes the assumption that sensing systems generate and communicate measurements instantaneously and suggests a linearized method for extracting information from latent measurements via a temporal measurement update that considers uncertainty in the measurement acquisition epoch. To investigate performance, numerical simulations are performed utilizing the consider/neglect extended Kalman filter framework applied to a lunar descent-to-landing scenario in which latent vision-based measurements with uncertain acquisition times are used to navigate the vehicle. Through Monte Carlo simulation and analysis, this paper shows that the presented approach can be used to maintain filter consistency for latent measurements with low measurement-time uncertainties. Furthermore, an error budget and sensitivity analysis are presented to provide insight into the impact of the measurement-time uncertainty on navigation performance. [ABSTRACT FROM AUTHOR]

Extended kalman filter (EKF) is widely used to integrated navigation system of global navigation satellite system (GNSS) and strapdown inertial navigation system (SINS). However, non-Gaussian noise and the uncertainty of measurement noise can seriously reduce the performance of EKF, so it is difficult to obtain an optimal GNSS/INS integration solution. At present, non-Gaussian noise processing is still a difficult issue in filter research. In this paper, an adaptive and robust maximum correntropy extended kalman filter (MCEKF) method based on Cauchy kernel function is proposed to solve the above problem. Thanks to the excellent properties of Cauchy kernel function, the proposed method can effectively avoid filter faults and has better stability. However, the performance of MCEKF depends on the select of kernel bandwidth parameter, which is a difficult problem in the engineering application of MCEKF. In this paper, the switching kernel bandwidth algorithm is employed to adaptively estimate the kernel bandwidth parameter to solve the tradeoff between convergence rate and steady-state misalignment in the MCEKF algorithm under constant kernel bandwidth. Finally, the performance of the proposed algorithm is verified by two sets of vehicle-mounted precise point positioning (PPP) and SINS tightly coupled integration experiments in urban environment. The results show that compared with EKF, the proposed method can significantly improve the positioning accuracy, that is, the PPP/SINS positioning accuracy based on GE, GR and GRE system is improved by 25.1 %, 2.5 % and 17.0 % in D196 experiment, and 23.5 %, 16.5 % and 31.8 % in D107 experiment, respectively. The velocity and attitude accuracy of the two methods are similar. The velocity errors of all schemes can reach cm/s level. The roll, pitch and heading errors of navigation-grade inertial sensor are all less than 0.12 degree. The roll and pitch errors of MEMS are less than 1.0 degree, the heading error of the G, GE, GR, and GRE systems are 1.719, 1.464, 1.676, and 1.475 degree, respectively. [ABSTRACT FROM AUTHOR]

The integrated navigation system based on the Global Navigation Satellite System (GNSS) in conjunction with the strapdown inertial navigation system (SINS) and the Doppler Velocity Logger (DVL) is essential for accurate and long-distance navigation in maritime environments. However, the error of the integrated navigation system gradually diverges due to the inevitable velocity measurement error of DVL when GNSS outages occur. To ensure the high navigational accuracy and stability of SINS, it is necessary to dynamically adjust the damping state of SINS provided externally. In this paper, we have developed a novel method for damping state switching based on machine learning with SINS. We construct a model of the change in reference velocity error and use sliding window technology to obtain the reference velocity error for model training. Before training, the digital compass loop is designed to process and highlight the change in reference velocity change errors. In order to reduce the impact of the damping switching, a variable damping system is used to transform the traditional one-time switching of the damping coefficient into a gradual switching, effectively reducing the impact of a sudden change in the damping coefficient on the system. Simulation experiments and tests on ships show that the proposed method effectively reduces the overshoot error integrated underwater during state switching. This research is of great importance for the optimal design of integrated underwater navigation systems. [ABSTRACT FROM AUTHOR]

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system. [ABSTRACT FROM AUTHOR]

The goal of this study is to determine the feasibility of a wearable multi-sensor positioning prototype to be used as a training tool to evaluate rowing technique and to determine the positioning accuracy using multiple mathematical models and estimation methods. The wearable device consists of an inertial measurement unit (IMU), an ultra-wideband (UWB) transceiver, and a global navigation satellite system (GNSS) receiver. An experiment on a rowing shell was conducted to evaluate the performance of the system on a rower's wrist, against a centimeter-level GNSS reference trajectory. This experiment analyzed the rowing motion in multiple navigation frames and with various positioning methods. The results show that the wearable device prototype is a viable option for rowing technique analysis; the system was able to provide the position, velocity, and attitude of a rower's wrist, with a positioning accuracy ranging between ±0.185 m and ±1.656 m depending on the estimation method. [ABSTRACT FROM AUTHOR]

The loop closure detection (LCD) methods in Unmanned Aerial Vehicle (UAV) Visual Inertial Navigation System (VINS) are often affected by issues such as insufficient image texture information and limited observational perspectives, resulting in constrained UAV positioning accuracy and reduced capability to perform complex tasks. This study proposes a Bag-of-Words (BoW) LCD method based on Neural Radiance Field (NeRF), which estimates camera poses from existing images and achieves rapid scene reconstruction through NeRF. A method is designed to select virtual viewpoints and render images along the flight trajectory using a specific sampling approach to expand the limited observational angles, mitigating the impact of image blur and insufficient texture information at specific viewpoints while enlarging the loop closure candidate frames to improve the accuracy and success rate of LCD. Additionally, a BoW vector construction method that incorporates the importance of similar visual words and an adapted virtual image filtering and comprehensive scoring calculation method are designed to determine loop closures. Applied to VINS-Mono and ORB-SLAM3, and compared with the advanced BoW model LCDs of the two systems, results indicate that the NeRF-based BoW LCD method can detect more than 48% additional accurate loop closures, while the system's navigation positioning error mean is reduced by over 46%, validating the effectiveness and superiority of the proposed method and demonstrating its significant importance for improving the navigation accuracy of VINS. [ABSTRACT FROM AUTHOR]

The effectiveness of road signs is hindered by obstructions, such as vegetation, mutual obstruction of signs, or the road alignment itself. The traditional evaluation of road sign recognition effectiveness is conducted through in-vehicle field surveys. However, this method has several drawbacks, including discontinuous identification, unclear positioning, incomplete coverage, and being time-consuming. Consequently, it is unable to effectively assess the recognition status of road signs at any arbitrary point within the road space. Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising, resampling, and three-dimensional model construction. Furthermore, a method for assessing the highway sign occlusion rate based on the picking algorithm was proposed. The algorithm was applied to an actual road environment, and the occlusion by other items was simulated. The results demonstrated the effectiveness of the method. This new method provides support for the fast and accurate calculation of road sign occlusion rates, which is of great importance for ensuring the safe traveling of vehicles. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel algorithm, the low error rate adaptive fading Kalman filter (LERAFKF), designed to predict system states in the presence of uncertainty in both the system matrix and the model. The purpose of developing the LERAFKF is to address challenges arising from measurement difficulties, system parameter uncertainties, and state–space model inaccuracies. Several studies have utilized the Kalman filter (KF) and extended Kalman filter (EKF) algorithms to handle uncertainties in system parameters, corrupted measurements with unknown covariances, and incorrectly defined system modeling. Our work distinguishes itself by proposing a new approach that achieves lower error and deviation rates by combining the current Kalman estimation algorithm and the fading factor adaptive filter. To achieve this goal, we transformed the KF into an adaptive KF by introducing a forgetting factor, and the algorithm was subsequently reconfigured to calculate an optimized forgetting factor. In this study, we conducted simulations and measurements using both linear and nonlinear systems. The linear system represents the motion of an object, and the simulation involved measurements from the inertial navigation system (INS) sensor, specifically the Pololu IMU01b three-axis inertial measurement unit (IMU) sensor. We employed the SDI33 system with 9 degrees of freedom (DoF) mounted on a three-axis rotary table for the nonlinear system. This system simulates a missile as a 4th-order nonlinear system. Our findings demonstrate that the proposed LERAFKF filter outperforms KF and EKF in estimating system states, particularly in measurement-related error scenarios. Mean square error analysis further confirmed that LERAFKF exhibited the lowest error values, showcasing superior performance over KF and EKF in linear and nonlinear systems. [ABSTRACT FROM AUTHOR]

With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability. [ABSTRACT FROM AUTHOR]

To address airflow interference and inertial altitude dispersion during high-speed aircraft flight, a novel algorithm integrating cubature Kalman filter (CKF), air data observation system, and inertial navigation system (INS) is proposed for real-time aircraft air data estimation through sensor data fusion. The algorithm utilizes nonlinear equations and models interactions among the inertial system, satellite positioning system, and air data system to integrate sensor data for computing aircraft velocity and altitude, followed by estimation of parameters such as angle of attack and sideslip. Experimental results demonstrate the high accuracy and stability of the proposed method in estimating airflow angle and Mach number. [ABSTRACT FROM AUTHOR]

Featured Application: In the last two decades, there has been an upward trend in obtaining redundant strap-down inertial navigators of reduced size and weight, which can be used on small vehicles (miniaturized satellites, miniaturized space robots, space rovers, MAVs, UAVs) or on vehicles that require onboard equipment with such properties (satellites, launch vehicles, missiles, aircraft, robots used in various industrial applications). A special application of the here-proposed methodology is the estimation of small satellites' attitude-based gyro measurements, providing, at the same time, a high degree of redundancy of the inertial detection unit. The significant technological changes related to the manufacturing of the miniaturized sensors produced a higher impact at the level of the detection units equipping the strap-down inertial navigation systems (INSs). Together with miniaturization, many more advantages are brought by these technologies, related to low costs, low necessary energy, high robustness and high potential for adapting the design solutions. However, reducing the dimensions and weight of the sensors is reflected by a decrease in their performance in terms of sensitivity, noise and the possibility of controlling sensitive elements. On the other hand, there is a permanent increase in the need to have in-space applications of miniaturized systems with a high degree of redundancy and to equip miniaturized satellites, miniaturized space robots or space rovers. The paper proposes a new methodology to increase the quality of the signals received from the miniaturized inertial measurement units (IMUs), but also to increase the degree of redundancy, by using low-cost sensors arranged in redundant linear configurations. The presentation is focused on the development of an attitude system based on strap-down inertial techniques which uses a redundant IMU equipped with three linear clusters of miniaturized gyros. For each of the three clusters, a data fusion mechanism based on the maximal ratio combining method is applied. This fusion mechanism reduces the noise power and bias of the signal delivered to the navigation processor. Shown are the theory, software modeling and experimentation results for the attitude algorithm, for the data fusion method, and for the integrated system. [ABSTRACT FROM AUTHOR]

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Land vehicle gravity anomaly measurement relies heavily on global navigation satellite system (GNSS). However, when gravity measurement is carried out in special environments such as forests, valleys and tunnels, GNSS observation quality will inevitably decline, which directly affects the accuracy of gravity anomaly measurement. From the point of view of the gravity anomaly measurement principle, obtaining accurate elevation information of the test line is the premise to ensure the accuracy of gravity anomaly measurement. Thus, this paper proposes a strapdown land vehicle dynamic gravity anomaly measurement method combining an odometer and a geo-information system. In this method, strapdown inertial navigation errors are suppressed by observing the velocity of the odometer output. Then, the position information obtained by the combined navigation is entered into the geo-information system to obtain the elevation. The results of a single test line show that the external coincidence accuracy of the proposed method is 1.65 mGal, and the accuracy is comparable to the traditional GNSS assisted land vehicle gravimetry method. In addition, compared with the odometer assisted land vehicle gravimetry method, the external coincidence accuracy is increased by 30%. [ABSTRACT FROM AUTHOR]

To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and cannot be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD/barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies. [ABSTRACT FROM AUTHOR]

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats. [ABSTRACT FROM AUTHOR]

Cooperative localization (CL) for air-to-ground robots in a satellite-denial environment has become a current research hotspot. The traditional distance-based heterogeneous multiple-robot CL method requires at least four unmanned aerial vehicles (UAVs) with known positions. When the number of known-position UAVs in a cluster collaborative network is insufficient, the traditional distance-based CL method has a certain inapplicability. A novel adaptive CL method for air-to-ground robots based on relative distance constraints is proposed in this paper. Based on a dynamically changing number of known-position UAVs in the cluster collaborative network, the adaptive fusion estimation threshold is set. When the number of known-position UAVs in the cluster cooperative network is large, the real-time dynamic topology characteristics of multiple robots' spatial geometric configurations are considered. The optimal spatial geometric configuration between UAVs and unmanned ground vehicles (UGVs) is utilized to achieve a high-precision CL solution for UGVs. Otherwise, in the event that the number of known-position UAVs in a cluster collaborative network is insufficient, distance observation constraint information between UAVs and UGVs is retained in real time. Position observation equations for UGVs' inertial navigation system (INS) have been constructed using inertial-based high-precision relative position constraints and relative distance constraints from historical to current times. The experimental results show that the proposed method achieves adaptive fusion estimation with a dynamically changing number of known-position UAVs in the cluster collaborative network, effectively verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods. [ABSTRACT FROM AUTHOR]

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ω APSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ω APSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs. [ABSTRACT FROM AUTHOR]

In many real navigation problems, moving objects may have some state or measurement constraints along their way. Using these constraints in conventional Extended Kalman Filter (EKF) equations results large matrices which are computationally time-consuming. In this paper, a new Constrained Navigation Filter (CNF) is proposed as a parallel to reduce the computational burden of the conventional EKF algorithm while increasing the positioning accuracy. So a methodology has been developed for Strap-down Inertial Navigation System (SINS) based on MEMS IMU applied on Pipeline Inspection Gauge (PIG) to sense data at constant sampling rate of 108 km of the pipeline. The results verified that using such a hybrid approach has improved positional accuracy 8.97% in comparison with that of the latest methods like EKF/ Pipe Line Junctions (PLJ). Also, the proposed method is 2.277 times better than EKF/PLJ in the algorithm runtime. [ABSTRACT FROM AUTHOR]

Indoor and outdoor positioning systems are difficult to locate in large areas and complex environments. In order to compensate for the impact of indoor and outdoor scene transformation, an indoor and outdoor positioning system based on GNSS/UWB/IMU is proposed, which can realize seamless and high-precision positioning between buildings in mixed scenes. In view of this, GNSS and IMU fusion is used outdoors for Error-State Kalman Filter (ESKF) filtering for positioning. At the same time, this paper proposes a D-CEP algorithm to analyze the UWB ranging variance offline and improve the accuracy of UWB positioning data. In the mixed scene, the UWB and GNSS information are tightly coupled and nonlinearly optimized to improve the positioning accuracy and achieve seamless connection between indoor and outdoor staggered scenes. Indoor and outdoor scenes use UWB and IMU fusion for ESKF filtering to achieve indoor and outdoor high precision. In indoor complex scenes, due to the existence of Ultra-wideband non line of sight (UWB-NLOS) error, the observation data may be discontinuous. When the number of observed signals is less than the number required for nonlinear optimization, the UWB-NLOS error is compensated by IMU prior information, and the positioning data is obtained to make up for the lack of positioning data and realize indoor and outdoor seamless positioning. Compared with the traditional filtering method, the average positioning accuracy is improved by 40.97 % (the Mean Absolute Error is increased from 0.256336 m to 0.151308 m). Compared with the state-of-the-art LiDAR-SLAM (LIO-SAM), the approximate positioning result can be obtained (ESKF-UWB-NLOS Mean Absolute Error is 0.151308 m, LiDAR-SLAM Mean Absolute Error is 0.148853 m). [ABSTRACT FROM AUTHOR]

The transfer alignment technique is very useful for the accurate initialization and calibration of gyroscopes and accelerometers of INS for air-launched tactical guided weapon systems. In the present war scenario, the initialization of INS should be accurate and rapid to launch the tactical weapon against air or land targets within the shortest available time. A lot of development has been carried out by researchers for INS transfer alignment in the field of state estimation. The study and method presented in the research are relevant to aerial launch vehicles. However, to meet present guidance requirements within less time for initializing weapons, more appropriate transfer alignment algorithms are needed. This paper discusses the relative performance of Kalman Filter (KF), Extended KF, and Unscented KF for aligning the weapon INS using the data from Master INS. New developments, limitations, applicability, and design methods for the Kalman filters, became a key component in the transfer alignment of air-launched tactical missiles. These methodologies are extensively utilized in Navigation and Control systems; therefore, this research work will be an informative and perfect guide to existing and potential readers. [ABSTRACT FROM AUTHOR]

In military applications, a highly accurate and precise navigation system is necessary for some ground-based combat system applications to navigate the vehicle and mark the location of the fuses and pickets laid by the mobile equipment. Few companies across the globe have developed expertise in manufacturing Global Navigation Satellite System (GNSS) receivers with Real Time Kinematics (RTK) capability along with integrated INS. On mobile equipment, it is not always possible to precisely place the Differential GNSS (DGNSS) antenna and GNSS receiver at the point for which the data needs to be marked. For such applications, lever arm calculation needs to be implemented. Also, due to uneven terrain conditions and slopes, the vehicle undulates up to 10o about its longitudinal and transverse axis. This dynamic condition induces considerable errors in the actual data. During the fuse laying process, there is also a continuous requirement for real-time location data in the GNSS-denied environment for a short period. INS was integrated with the GNSS receiver. We discuss test methodology for GNSS receiver performance evaluation. Experimentations were performed, taking into consideration these requirements. Data collected are also analyzed and discussed. Test results confirmed that the module is accurate with an accuracy of a few centimeters. [ABSTRACT FROM AUTHOR]

In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) can provide an approach for spacecraft autonomous navigation in earth–moon space to make up for the insufficiency of earth-based tracking, telemetry, and control systems. However, its weak power and poor observation geometry near the moon causes new problems. After the GNSS signal characteristics and satellite visibility were evaluated in Phasing Orbit and Lunar Transfer Orbit, we proposed an adaptive Kalman filter based on the Carrier-to-Noise ratio (C/N0) and innovation vector to weaken the influence of GNSS accuracy attenuation as much as possible. The experimental results show that the spacecraft position and velocity accuracy are better than 10 m and 0.1 m/s near the Earth, and better than 50 m and approximately 0.2 m/s near the moon use GNSS with the proposed adaptive algorithms. Additionally, because of the deterioration of navigation performance based on the orbit filter during orbital maneuvering, we used accelerometer data to compensate for the dynamic model to maintain navigation performance. The results of the experiment provide a reference for subsequent studies. [ABSTRACT FROM AUTHOR]

When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved. [ABSTRACT FROM AUTHOR]

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE (3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE (3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker–Campbell–Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE (3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE (3) group-based model. [ABSTRACT FROM AUTHOR]

Aeromagnetic surveys are widely used in geological exploration, mineral resource assessment, environmental monitoring, military reconnaissance, and other areas. It is necessary to perform magnetic compensation for interference in these fields. In recent years, large unmanned aerial vehicles (UAVs) have been more suitable for magnetic detection missions because of the greater loads they can carry. This article proposes some methods for the magnetic compensation of large multiload UAVs. Because of the interference of the large platform and instrument noise, the standard deviations (stds) of the compensation data used in this paper are larger. At the beginning of this article, using the traditional T-L model, we avoid the shortcomings of the anti-magnetic interference ability of triaxial magnetic gate magnetometers. The direction cosine information is obtained by using an inertial navigation system, the global positioning system, and a triaxial magnetic gate magnetometer. Then, we increase the amplitude of the maneuvers in the compensation process; this reduces the multicollinearity problems in the compensation matrix to a certain extent, but it also results in greater magnetic field interference. Lastly, we employ the method of Lasso regularization Newton iteration (LRNM). Compared to the traditional methods of least squares (LS) and singular value decomposition (SVD), LRNM provides improvements of 34% and 27%, respectively. In summary, this series of schemes can be used to perform effective compensation for large multi-load UAVs and improve the actual use of large UAVs, making them more accurate in the measurement of aeromagnetic survey data. [ABSTRACT FROM AUTHOR]

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial. [ABSTRACT FROM AUTHOR]

Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively. [ABSTRACT FROM AUTHOR]

In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars introduce various nonlinear filtering methods to solve this problem, but these filtering methods not only have large amount of calculation, but also are low accuracy. In theory, standard Kalman filtering can obtain the optimal estimate of the state under the condition that the structural parameters and noise statistical characteristic parameters of the random system are accurately known. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. While performing state estimation, it can also estimate the system's noise parameters in real time through measurement output. The experimental results show that the improved algorithm has high anti-interference and anti-divergence ability. Under the simulation conditions, pitch, roll and heading errors can be stabilized at about 10 degrees. The real measurement with Redmi K30Pro and iPhone 14 mobile phone shows that the alignment result of the improved algorithm is consistent, and the difference of multiple alignment results is stable within 5 degrees, while the difference of traditional method is more than 20 degrees. It can be applied in smart phone navigation with the strapdown inertial navigation system. [ABSTRACT FROM AUTHOR]

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states' variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment. [ABSTRACT FROM AUTHOR]

As an effective tool for refining local gravity data with mGal accuracy, vehicle gravimetry system usually consists of Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). However, the traditional system is unable to achieve gravimetry in an environment without GNSS signal dues to heavy dependence on GNSS signal. To solve this problem, this study proposed a gravimetry method specifically for the environment without GNSS signal, which consists of the high-precision Strapdown Inertial Navigation System (SINS) and Laser Doppler Velocimeter (LDV) developed by our unit. Based on the error analysis, the system index requirements were proposed, and the relevant indexes of the LDV are verified through simulation and vehicle experiment. The simulation results for different velocimeter accuracies show that the internal accord accuracy of the system is within 1mGal when the velocimeter accuracy is 1 ‰, and the internal accord accuracies of the system are more than 1mGal when the velocimeter accuracies are 5 ‰ and 10‰. The results of the vehicle experiment for different dimensional velocimeters show that when the spatial resolution is 1.7 km, the SINS/LDV (2D) and SINS/GNSS integrated systems have an internal accord accuracy of 0.68 mGal and 0.54 mGal, respectively, while the SINS/LDV (1D) integrated system has an internal accord accuracy of 1.50 mGal. The feasibility of the SINS/LDV system and the necessity of the SINS/LDV system for the dimension and accuracy of the velocimeter are illustrated. [ABSTRACT FROM AUTHOR]

Recent advances in sensor and algorithmic technologies have led to the exploration of remote/proximal sensing for automated forest inventory at various scales. For detailed below-canopy mapping to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume, under-canopy mobile LiDAR mapping is preferred. These mapping systems typically rely on an onboard integrated global navigation satellite system/inertial navigation system (GNSS/INS) unit for point cloud generation. The main challenge of such under-canopy mapping is the intermittent access to the GNSS signal, which is crucial to deriving accurately georeferenced mapping products. Advances in Simultaneous Localization and Mapping (SLAM) offer an alternative to GNSS/INS-assisted georeferencing in GNSS-denied/challenging scenarios. In this study, we propose a general and comprehensive Forest Feature LiDAR SLAM framework that encompasses odometry and mapping threads for a 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventories. In the odometry thread, ground/tree trunk features are extracted from LiDAR scans (i.e., points from a full revolution of the laser beams) and used to estimate the transformation between successive scans. The mapping thread performs local/global least squares adjustment (LSA) using derived features to register LiDAR scans to a common reference frame. One advantage of the proposed framework is that the tree trunk features and ground information – which are critical to forest inventory applications – are directly derived in the SLAM process, making it superior to other geometric feature-based approaches. Additionally, relative trajectory information provided by onboard navigation units and/or reference point clouds from other sources can be incorporated into the process. In this study, three in-house developed backpack systems with varying specifications were used to collect data in complex forest areas. The proposed SLAM strategy was performed on these datasets and compared with point clouds acquired by uncrewed aerial vehicle (UAV) and a commercial backpack LiDAR system. Experimental results suggested that the proposed framework can produce point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) and positional accuracy (around 10 cm) for all backpack systems. [ABSTRACT FROM AUTHOR]

Satellite navigation is the backbone of maritime navigation today. However, the technical vulnerability of on-board Global Navigation Satellite System (GNSS) receivers the satellite system greatly destabilizes maritime security due to the loss of ship's position and accurate time. This article devoted to study an alternative method for obtaining coordinates and accurate time based on the use of automatic identification system (AIS) radio channels, so-called range mode (R-Mode). We use other AIS ship stations with reliable position data as reference stations and determine time of arrival for received AIS transmissions. To improve the accuracy of measuring signal arrival instance in the time difference of arrival (TDOA), that we utilize for trilateration, it is proposed signal oversampling and applying the fast Fourier transform (FFT) to the product of quadrature components of the baseband Gaussian minimum shift keying (GMSK) signal in the window of AIS time slot. To take into account the movement of other ships, appropriate coordinate corrections are foreseen, which can be calculated by dead reckoning or by the inertial navigation system of our ship. The proposed method is fully compatible with the existing AIS signals and may be employed in critical situations of locally limited (jamming, spoofing) GNSS abilities. It can be implemented as a separate unit, working for receiving in parallel with the mandatory AIS transponder. [ABSTRACT FROM AUTHOR]

Indoor positioning is a key technology in today's intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimummean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise, especially heavy-tailed noise, the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF. The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform (UT) and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC. Subsequently, the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations. Moreover, to mitigate the influence of non-line-of-sight (NLOS) errors positioning accuracy, this paper proposes a k-medoid clustering algorithm based on bisection k-means (Bikmeans). This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation. Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)