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Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

This paper aims to design a method of multi-source data fusion in multi-target collaborative navigation. First, the respective features of GPS/INS/RS/MO data in the navigation process are clarified...

Information fusion of the GPS/INS integrated system is always related to characteristics of the inertial system and the sensor feature, yet prior knowledge is still difficult to obtain in real applications. To deal with the uncertainty of error covariance and state noise in vehicle navigation, this paper presents a novel approach, wherein the integration of Square-root Cubature Kalman Filters (SCKF) and Interacting Multiple Model (IMM) are also introduced. In the framework of IMM, the SCKFs with different covariance are designed to reflect various vehicle dynamics. Besides, since the IMM-SCKF can switch flexibly among the filters, the transition probability matrix is computed with maximum likelihood method to adapt to different noise characteristics. The performance of the proposed algorithm is guaranteed by theoretical analyses, and a series of vehicular experiments with different maneuvers are carried out in an urban environment. The results indicate that, in comparison with the CKF and the IMM-CKF, the accuracy of velocity and attitude are increased by the proposed strategy.

Low-cost GPS/INS integration system is the ideal combination of navigation and positioning. However, the sensitivity of low-cost INS is not good enough for the initial alignment in statics base bef...

Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton’s laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a low-cost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.

SUMMARYIn mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments.

In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.

Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS, and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably.

It is the main challenge for Global Positioning System (GPS)/Inertial Navigation System (INS) to achieve reliable and low-cost positioning solutions during GPS outages. A new GPS/INS hybrid method is proposed to bridge GPS outages. Firstly, a data pre-processing algorithm based on empirical mode decomposition (EMD) for wavelet de-noising is developed to reduce the uncertain noise of IMU raw measurements and provide accurate information for subsequent GPS/INS data fusion and training samples. Then, the interactive multi-model extended Kalman filter(IMM-EKF) algorithm is proposed to improve the robustness of Kalman filter output and the accuracy of model training target output. Finally, a new intelligent structure of GPS/INS based on Extreme Learning Machine (ELM) is proposed. When the GPS is available, the IMM-EKF is used to fuse the GPS and de-noised INS data, and the de-noised INS data and the outputs of IMM-EKF are used to train the ELM. During GPS outages, the ELM is used to predict and correct the INS position error. In order to evaluate the effectiveness of the proposed method, 3 tests were performed in the actual field test. The comparison results show that the proposed fusion method can significantly improve the accuracy and reliability of positioning during GPS outages.

We present a LiDAR-based SLAM aided by GPS/INS integrated navigation system which improves the robustness of real-time mapping and localization for autonomous vehicles. We add the pose of GPS/INS as a penalty term in the scan-to-map registration to obtain high precision localization and clear map simultaneously. When the scan-to-map registration failed, we use the pose of GPS/INS to create a new submap and apply the map-to-map registration to register between the submaps, which improves robustness. Because the map-to-map registration cannot run in real-time, we generate the global map and local map at different frequency levels, which match the requirements of global and local path planning respectively. The experimental results show that our method has more robust performance and better map quality than the existing methods.

In this paper, a novel GPS/INS integrated navigation system is proposed based on long and short term memory neural network (LSTM NN) for the unmanned aerial vehicle (UAV). UAV navigation system is a kind of nonlinear system usually consisting of inertial measurement unit (IMU) and global positioning system (GPS), which estimates the position and speed of the UAV by Kalman filters. However, the covariance noise parameter in Kalman filters is difficult to set manually, and moreover which should dynamically adapt to the UAV motion. LSTM NN has particular contextual memory units which can store the previous useful information automatically and is selected to design a novel end-to-end navigation method. To improve the accuracy of the model, different configurations of the LSTM NNs are used for comparison. Experimental results demonstrate that the proposed data-driven navigation method can achieve the online UAV position estimation well without building accuracy mathematical model and adjusting parameters manually.

A covert spoofing algorithm of an unmanned aerial vehicle (UAV) based on GPS/inertial-navigation-system-integrated navigation is analyzed in this paper. The proposed algorithm theoretically proves the fact that when the acceleration component of the counterfeit GPS signal is the difference between the UAV's current acceleration and the spoofing control input, the UAV can be covert spoofed. To avoid adverse consequences (detection or crash) caused by frequent changes in UAV flight during the GPS spoofing attacks, the proposed algorithm requires that the deception trajectory planned by the GPS spoofer is slowly changing relative to the reference trajectory, which is pre-set by the UAV. Simulation results have verified the correctness of the proposed covert spoofing algorithm of UAV. Moreover, the spoofing effect is more distinct when the deception trajectory planning is considered.

The paper is devoted to questions on increasing of noise immunity of GPS/INS navigation systems containing GPS and inertial navigation systems. Analysis of effectual methods for digital processing of noisy signals of GPS/INS navigation systems is actual task. At the same time there is another direction of development of GPS navigation systems, based on modification of aerial tract and filtration devices. Different adaptive methods of multi-aerial receipt can be stressed out as an example. As regards the filtration method, there are some additional possibilities for filtration of noisy GPS signals upon presence of a priory or a posteriori data on energy-frequency characteristics of noise signal. The comparative analysis of methods for increasing of noise immunity of integrated GPS/INS navigation systems is carried out. The possibility for optimization of averaged integrated value of modified indicator, named as effective ratio of power of carrier signal on noise power regarding the case of utilization of noise signals pertaining to broad class of noise signals characterized by presence of integrated limitation condition imposed on amplitude-frequency spectrum. The optimization task on increase of averaged integrated parameter indicating the effective value of ratio of power of carrier signal on power of noise signal is formulated. Solution of formulated optimization task make it possible to formulate recommendations for increasing the noise immunity of GPS/INS based navigation systems for some limited class of utilized jamming noise. The methodic to increase noise immunity of GPS targeting systems in condition of jamming noise is suggested., Статья посвящена вопросам повышения помехоустойчивости GPS/INS навигационных систем. Изложен предлагаемый новый способ повышения помехоустойчивости GPS навигационных систем. Приведён сравнительный анализ методов повышения помехоустойчивости интегрированных GPS/Ins систем навигации. В качестве одного из основных показателей отношения сигнал-шум выделен модифицированный показатель, известный под названием эффективное отношение мощности несущего сигнала к мощности шумов. Сформулирована оптимизационная задача повышения среднеинтегрированного показателя «эффективная величина» отношения мощности и несущего сигнала к мощности шумового сигнала. Рассмотрена возможность оптимизации среднеинтегрированной величины рассматриваемого модифицированного показателя в случае применения в качестве шумового сигнала представителя достаточно широкого класса помеховых сигналов, характеризующихся наличием интегрального ограничения на амплитудно-частотный спектр. С учетом исходного целевого функционала и интегрального ограничительного условия, наложенного на шумовые сигналы, составлен целевой функционал безусловной вариационной оптимизации. Решение сформулированной оптимизационной задачи с применением условий уравнения Эйлера-Лагранжа позволило выработать рекомендацию по повышению помехоустойчивости GPS основанных навигационных систем для некоторого ограниченного класса используемых заглушающих помех. На основании полученных результатов составлена методика повышения помехоустойчивости GPS систем наведения в условиях применения заглушающей помехи рассмотренного класса., Статтю присвячено питанням підвищення завадостійкості GPS/INS навігаційних систем, що включають в себе GPS та інерціальні навігаційні пристрої. Пошук ефективних шляхів цифрової обробки зашумлених сигналів навігаційних систем є актуальним завданням, разом з тим, існує також інший напрямок розвитку GPS навігаційних систем, що заснований на удосконаленні антенного тракту і кутів фільтрації. Сюди можна віднести різні адаптивні методи рознесеного прийому. Що стосується методу фільтрації, то тут існують деякі додаткові можливості фільтрації зашумленного GPS сигналу за наявності апріорних або апастеріорних даних про енергочастотні характеристики шумового сигналу. Наведено порівняльний аналіз методів підвищення завадостійкості інтегрованих GPS/Ins систем навігації. Розглянуто можливість оптимізації середньоінтегрірованної величини модифікованого показника, названого ефективним відношенням потужності сигналу до потужності шумів відносно до випадку застосування в якості шумового сигналу представників досить широкого класу сигналів завад та характеризуються наявністю інтегрального обмеження на амплітудно-частотний спектр. Сформульована оптимізаційна задача підвищення средньоінтегрірованого показника «ефективна величина» відношення потужності сигналу до потужності шумового сигналу. Рішення сформульованої оптимізаційної задачі дозволило виробити рекомендації щодо підвищення завадостійкості GPS / INS заснованих навігаційних систем для деякого обмеженого класу використаних перешкод. Запропоновано методику підвищення завадостійкості GPS систем наведення в умовах застосування перешкод.

Fault detection and identification (FDI) in either a stand-alone GPS or in integrated GPS/INS systems is essential for improving the quality of positioning, navigation, and many other applications. The assumption that the observations include a single fault has been considered intensively in literature. However, this assumption may not necessarily be valid due to the fact that multiple faults may exist simultaneously. In this study, separability of multiple faults in GPS/INS integration systems has been analysed geometrically and statistically. This has been achieved through testing how large correlation coefficient between any pair of fault tests statistics increases the probability of faults misidentification. In addition, a new calculation procedure of correlation coefficient when four faults appear in the observations has been developed. This procedure considers calculation the correlation between a single and a punch of measurements combined together. The results show that there is a strong relationship between the value of correlation coefficient and the probability of misidentification. Furthermore, a significant relationship between the correlation and the fault test values can be found when splitting the measurements combinations into groups based on the combination similarity. Nevertheless, this relationship can be defined without splitting the measurements into groups when using a new correlation procedure for four faults case. The geometric representation shows that large correlation coefficient reflects small angle between the correlation and the x-axis; whereas the angle between the fault-test vectors and the x-axis becomes wider when a tiny correlation exist.

The extended Kalman filter (EKF) had widely been used in the inertial navigation system (INS) and global positioning system (GPS) integrated navigation system. Nevertheless, the EKF contains instability caused by linearisation and numerous calculations of the Jacobian matrix. To solve the problem, the adaptive interacting multiple model (AIMM) filter algorithm is proposed in order to achieve a better navigation solution. The soft-switching feature, which is offered by interacting multiple model, allows conversion of process noise between lower and upper limits, meanwhile the measurement covariance is adjusted online by Sage adaptive filter. Considering the need to update the pseudo-range and Doppler data, the updating strategy of classification measurement is proposed. The results of the GPS/INS integrated navigation are estimated in the form of data of real ship, and experimental results indicate that the higher position accuracy can be obtained in AIMM filter.

Multi-sensor integrated positioning technique that combines complementary features of the global positioning system (GPS) and inertial navigation system (INS) for navigation in challenging urban environments has been a hot research area. A variety of algorithms have been proposed over the past two decades for this well-studied field. However, with the increasing demands of seamless positioning, traditional GPS/INS integrated technique faces rigorous challenges, especially in GPS-denied environment, where traditional techniques cannot be applied directly. To improve the precision and robustness of the navigation system, a novel hybrid GPS/INS/Doppler velocity log (DVL) positioning method is proposed, which introduces DVL as the reference information to assist the GPS module to correct the divergence error of INS. A new robust adaptive federated strong tracking Kalman filter (RAFSTKF) algorithm is also presented for data fusion, which has the advantage of robustness with respect to the uncertainty of the system model. Meanwhile, we introduce the least square principle and adaptively adjust information sharing factors to obtain the optimal estimation, which can improve the reliability of the overall system. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed hybrid GPS/INS/DVL positioning method based on RAFSTKF. In addition, the tracking performance of the proposed method outperforms that of traditional federated Kalman filter.

In GPS/INS integrated navigation, Kalman filter is usually used for data fusion between GPS and INS. The filtering algorithm of integrated navigation is related to whether the advantages of each sensor in the integrated navigation can be utilized, the navigation accuracy is improved, the reliable working time of the navigation system is improved and the navigation requirement are met. In this paper, we proposed an approach based on the Grasshopper optimization algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarm of grasshoppers. The goal of the proposed approach is to optimize the parameters of the Kalman filter. The Kalman filter method is improved by grasshopper optimization algorithm, which improves the accuracy of integrated navigation and reduces the errors caused by system noise and measurement noise. For verification, the proposed approach is compared with particle swarm optimization. The simulation experiment results show that the proposed approach has a better effect.

Number of visible satellites and mask angle of the GPS satellites are two major factors to influence positioning error characteristics of GPS/INS integrated navigation. For GPS signals, there are shading effects in urban canyons and forest environments. Based on the positioning error characteristics of GPS/INS integrated navigation model, with the different number of the visible satellites under the influence of mask angle, the simulation of positioning error RMS with different number of the visible satellites is provided. At the same time, the characteristics of the simulation results are analyzed in detail. Some theoretical references for the influence of mask angle to the GPS/INS integrated navigation performance are provided.

This dissertation focus on achieving high accuracy navigation using low-cost sensors and on high precision trajectory tracking control for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs).For high accuracy navigation, this dissertation presents a real-time sliding-window estimator to tightly integrate Differential-GPS (DGPS) and inertial measurement unit (IMU) to achieve reliable, high precision navigation performance in GPS-challenged urban environments using a low-cost, single-frequency (L1) GPS receiver. The approach is novel in that it utilizes the phase measurements, without resolving the integer ambiguity, to improve the accuracy and the robustness of the estimation results. Experimental results demonstrate that the performance of the proposed navigation system is significantly better than the extended Kalman Filter (EKF) (improved by one order of magnitude) and the novel usage of phase measurements further improves the robustness of the estimator to the pseudorange multipath error, which could otherwise be several meters in urban environments.Regarding precision trajectory tracking, this dissertation presents a new command filtered backstepping technique for under-actuated VTOL UAVs. Quaternions are used to represent the attitude of the vehicle to ensure the global attitude tracking without singularities. Since the quaternions have their own unique algebra, they cannot be filtered by a vector-based command filter; therefore, a second-order quaternion filter is developed to filter the quaternion and automatically compute its derivative, which determines the commanded angular rate vector. A quadrotor vehicle is used as an example to show the performance of the proposed controller.

The extended Kalman filter (EKF) is widely used for the integration of the global positioning system (GPS) and inertial navigation system (INS). It is well known that the EKF performance degrades when the system nonlinearity increases or the measurement covariance is not accurate. For the loosely coupled GPS/INS integration, accurate determination of the GPS measurement and its covariance is not a simple task. An extended state observer (ESO) is proposed for the first time to improve the navigation performance of the loosely coupled GPS/INS integration with accurate measurement modeling. The performance of the proposed method is comprehensively evaluated and analyzed, and comparisons were made with respect to the standard EKF method. Simulations and a field vehicular test were conducted to evaluate the performance of the integrated system using the proposed algorithm. The results demonstrate that the navigation performance of the proposed method can be improved significantly in terms of position and velocity when compared with the EKF method. In the simulation test, the RMS values of the positioning errors are reduced by 52.57%, 48.56%, and 34.16% in the north, east, and vertical directions, respectively. The corresponding percentage for the velocity errors are 48.20%, 43.17%, and 22.65%, respectively. In the field test, the RMS values of the positioning errors are decreased by 40.91%, 47.63%, and 12.21%, respectively. The corresponding percentage for the velocity errors are 42.13%, 31.38%, and 33.86%, respectively. The improvement of attitude accuracy is not obvious as it mainly relies on the quality of the inertial sensors.

This paper presents an integrated navigation algorithm based on Kalman filter in underground garage. Traditional vehicular navigation algorithms are mainly employed in passenger vehicles traveling on the ground. Vehicles are generally equipped with Global Positioning System(GPS) and Inertial Navigation System(INS) to provide accurate positioning data for vehicles. However, the GPS receiver cannot acquire the positioning signal from the satellite, due to the ground barrier, after the vehicle enters the underground garage. The GPS/Inertial Navigation System(INS) integrated navigation cannot work normally. Inertial navigation can only calculate the position data in a short time, and the positioning accuracy diverges after a period of time. In this case, this paper introduces the integrated navigation algorithm of Light Detection and Ranging(LIDAR) scanner and inertial device. The LIDAR/INS integrated navigation will take place the indoor Dead Reckoning(DR) algorithm. The accuracy and stability of LIDAR scanner indoor navigation and positioning provide calibrated positioning for inertial navigation in Kalman filter. Finally, continuous indoor and outdoor seamless vehicle navigation driving trajectories are achieved by the navigation system.