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To address the issue of integrity monitoring for a multi-source information fusion navigation system, a theoretical framework of two-level integrity monitoring is proposed. Firstly, at the system level, a system-integrity-monitoring method based on the Kalman filter weighted least-squares form is established to detect and isolate faulty navigation sources. Secondly, at the sensor level, considering the redundancy of the faulty navigation sources, this paper presents the design of a multi-mode comprehensive fault-detection method for non-redundant navigation sources. Additionally, an extended-dimension matrix optimized sensor-fault detection and verification method for redundant navigation sources is proposed. Finally, integrity risk allocation criteria are established based on the effectiveness of navigation sources to calculate the system protection level and trigger integrity alarms. The two-level integrity-monitoring method was tested on a multi-source information fusion navigation system integrated with an inertial navigation system (INS), global positioning system (GPS), BeiDou satellite navigation system (BDS), Doppler velocity log (DVL), barometric altimeter (BA), and terrain-aided navigation (TAN). Test results demonstrate that the proposed method can effectively isolate the faulty navigation source within 10 s. Furthermore, it can detect the faulty sensors within the faulty navigation sources, thereby enhancing the reliability and robustness of the multi-source information fusion navigation system. [ABSTRACT FROM AUTHOR]

The inertial Navigation Systems/global navigation satellite system (SINS/GNSS) has become a research hotspot in the field of train positioning. However, during a uniform straight-line motion period, the heading misalignment angle of the SINS/GNSS is unobservable, resulting in the divergence of the heading misalignment angle and ultimately causing a divergence in the train's speed and position estimation. To address this issue, this paper proposes an estimation and compensation method for the heading misalignment angle for train SINS/GNSS integrated navigation system based on an observability analysis. When the train enters a straight-line segment, the alignment of the train's sideslip angle and the satellite velocity heading angle allows the achievement of velocity heading observation values that resolve the issue. In a curved segment, the heading angle becomes observable, allowing for an accurate estimation of the SINS's heading misalignment angle using GNSS observations. The results showed that, whether the train is on a straight or curved track, the position estimation accuracy meets the simulation design criteria of 0.1 m, and the heading accuracy is better than 0.25°. In comparison to the results of pure GNSS position and velocity-assisted navigation, where heading divergence occurs during constant velocity straight-line segments, the method proposed in this paper not only converges but also achieves an accuracy comparable to the GNSS velocity-based heading alignment. The simulation results demonstrate that the proposed strategy significantly improves the accuracy of the heading misalignment angle estimation, thereby enhancing the accuracy of speed and position estimation under a GNSS-denied environment. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved. [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]

Navigation systems are the basis of trajectory tracking and autonomous vehicle motion control for intelligent vehicles. A fully constrained integrated navigation system (FC-NS) provides a complete constraint to an inertial navigation system (INS). However, the performance of FC-NSs with conventional information-sharing schemes worsens as faults appear. Taking the fusion of INS and multiple global navigation satellite systems (multi-GNSSs) as an example, this paper proposes a fault-tolerant method to improve the navigation performance of FC-NS under fault conditions. The contributions of this paper are twofold. First, a conditional reset information-sharing scheme, which was introduced based on analyzing the shortcomings of traditional schemes, prevents federate filters from error tracking, and variance amplification is proposed. Second, a novel fault detection algorithm to improve the fault detection performance is designed. Based on the residual topology extracted by a self-organizing incremental neural network (SOINN), a rough surface for the detection of abnormal measurements is constructed through primary neurons and the maximum distance between them. The simulation results indicate that the proposed conditional reset scheme realizes FC-NS data fusion in normal conditions and achieves a 23% higher fault detection ratio than classic schemes under large faults. The fault detection ratios of the proposed fault detection algorithm are 8.5, 60.5 and 12.5% higher than those of the chi-square test under 30, 40 and 50 m faults, respectively, which obviously improves the positioning accuracy of FC-NS. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study utilizing the vision-aided inertial navigation system (VaINS) on a fixed-wing unmanned aerial vehicle (UAV). VaINS operates as a data-logging box, collecting sensor data, processing them, and transmitting the navigation solution to an operator terminal in real time. The flight data contain buildings, vegetation, a river, a lake, and mountains. The camera's pitch angle was tested at both a grazing angle of 22° and a nadir angle of 90°. The aim of this experiment was twofold: to explore the application of VaINS on a fixed-wing UAV and to assess the performance of the image-aided navigation system. The image-aided inertial navigation system (INS) solution was compared with a reference solution during GNSS-denied time intervals. The camera's pitch angle at 90° exhibited a slight advantage over the grazing angle at 22°. Moreover, in the linear flight segments, the image-aided navigation system demonstrated significantly better performance compared to the free inertial solution during the GNSS-denied periods. [ABSTRACT FROM AUTHOR]

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone. [ABSTRACT FROM AUTHOR]

For the integrity monitoring of a multi-source PNT (Positioning, Navigation, and Timing) resilient fusion navigation system, a theoretical framework of multi-level autonomous integrity monitoring is proposed. According to the mode of multi-source fusion navigation, the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level. For isolated non-redundant navigation sources, the system level recovery verification model is used. For the isolated multi-redundant navigation sources, the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors, and the isolated fault sensors are verified in real-time. Finally, according to the fault detection and verification results at each level, the observed information in the fusion navigation solution is dynamically adjusted. On this basis, the integrity risk dynamic monitoring tree is established to calculate the Protection Level (PL) and evaluate the integrity of the multi-source integrated navigation system. The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System (INS), Global Navigation Satellite System (GNSS), Long Baseline Location (LBL), and Ultra Short Baseline Location (USBL). The test results show that the proposed method can effectively isolate the fault source within 5 s, and can quickly detect multiple faulty sensors, ensuring that the positioning accuracy of the fusion navigation system is within 5 m, effectively improving the resilience and reliability of the multi-source fusion navigation system. [ABSTRACT FROM AUTHOR]

With the continuous development of positioning technology in today's world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction. [ABSTRACT FROM AUTHOR]

High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle's onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems' drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from 19.4 m to 3.3 m with 82.98 % improvement and the 2D-position max error reduced from 73.9 m to 14.2 m with 80.78 % improvement. [ABSTRACT FROM AUTHOR]

The occlusion of shade leads to the intermittent reception of satellite signals, which subsequently results in a decline of the positioning accuracy of Global Navigation Satellite System (GNSS). In order to improve the accuracy of positioning and ensure the continuity of navigation results in weak signal environment, a cubature Kalman Filter (CKF)-based micro-electro mechanical-system (MEMS) grade inertial navigation system (INS) assisted GNSS vector tracking (VT) loop is proposed in this paper. Combining effective GNSS measurements and INS information, attitude, velocity, and position results are predicted by CKF, and the pseudo-range results from navigation filter are fed back into the VT loop. The field tests show that the proposed CKF-based MEMS-INS assisted GNSS VT loop can run continuously and stably in weak signal environment with higher navigation accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles. [ABSTRACT FROM AUTHOR]

Ubiquitous positioning using combined lowcost, Micro-electro-mechanical-systems (MEMS) Inertial Navigation Systems (INS) and Global Navigation Satellite System (GNSS) systems remains a challenge in difficult GNSS environments. This is due to the GNSS signals being partially or completely obstructed by building structures and consequently no information being available to calibrate the MEMS INS errors. In these environments although there may be insufficient signals for a two or three dimensional GNSS solution, some signals can still be received, particularly when utilizing high sensitivity GNSS receivers. These signals can be used to aid the INS/GNSS integrated system but can only be realized when it utilizes a tightly coupled (TC) as opposed to a loosely coupled (LC) integration architecture. This is particularly useful for MEMS based INS/GNSS integrated system as its performance can degrade rapidly over short period of GNSS outages. This paper aims to evaluate the capabilities of MEMS based INS/GNSS using TC integration architecture during extended period of GNSS outages. The results obtained show that TC integration architecture does help limit the error growth even when INS/GNSS integrated system experienced relatively long period of partial GNSS outages. commercially available MEMS INSs. Two independent datasets were collected and used to demonstrate the performance of the integrated system architecture (LC vs. TC) during periods of complete and partial GNSS outage. The results of these experiments conducted show that considerable improvements are observed when TC integration architecture is employed compared to LC. The experiment platform, integrated processing architecture and results obtained will be fully presented in this paper. [ABSTRACT FROM AUTHOR]

Complex and fatal road vehicle crashes are often a matter of dispute in forensic investigation. The proposed algorithms and methods, utilizing data from inertial measurement units and satellite navigation, enable road–vehicle trajectory reconstruction in 3-D suitable for postaccident forensic analysis. This paper provides an estimate of the reliability of this approach and its relation to component specifications. We start by describing an appropriate type of the strap-down inertial navigation system before providing a detailed performance analysis when the system is integrated with global navigation satellite systems. In addition, we present custom hardware and embedded software solutions, as well as experimental evaluation in real-world case study, capturing typical crash scenario performed at an approved safety performance assessment laboratory. In our experiments, strap-down inertial mechanization was used for short-term vehicle trajectory reconstruction using sensor readings and compared with ground-truth position captured on video. We have used reference equipment to validate the approach and the experiment resulted in a positioning accuracy on a decimeter level. [ABSTRACT FROM PUBLISHER]

In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected random observed quantities which may appear in a dynamic model. In particular, this paper will consider the case of vision-based navigation. This algorithm is equipped with quadratic constraints and makes use of condition equations. The paper will show that the refined data from different sensors including a Global Positioning System (GPS) receiver, an Inertial Navigation System (INS) and remote sensors can be fused into a Constrained Total Extended Kalman Filter (CTEKF) algorithm. The CTEKF algorithm is applied to a case study in the Guilan province in the north of Iran. The results show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs). [ABSTRACT FROM AUTHOR]

The integration of the Global Navigation Satellite System (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information using a data fusing algorithm such as a Kalman filter. There has been much interest in the absolute accuracy with dominant components of mid-term and long-term errors in current research works related to the GNSS/INS navigation systems, whereas few research works focus on the relative accuracy on different time scales. However, new applications of GNSS/INS integration, such as position and orientation system for mobile mapping systems and the GNSS/INS deeply-coupled receiver, require the relative accuracy on different time scales, especially that of the short-term accuracy. This paper raises the importance of the short-term accuracy of the GNSS/INS systems in certain applications. Current methods to evaluate the navigation accuracy are mainly to provide some statistical values that can represent the overall error level (e.g., RMS), but cannot show the relative accuracy. Allan variance is a method of representing root mean square (RMS) random drift error as a function of average time. An Allan variance analysis method is proposed in this paper to evaluate the relative accuracy on different time scales of GNSS/INS systems. The feasibility of the idea was verified by the road test results of different grades of GNSS /INS systems. The results show that Allan variance can give the levels of the navigation accuracy of the GNSS/INS systems on different time scales in an effective way, especially for the short-term accuracy. [ABSTRACT FROM AUTHOR]

Purpose |!|#8211; The purpose of this paper is to develop an integrity monitoring method using ERAIM (Extended Receiver Autonomous Integrity Monitoring) for the integrated GNSS/Inertial (Global Navigation Satellite System and inertial navigation system) of general aviation aircraft. Design/methodology/approach |!|#8211; First the tightly integrated GNSS with Strapdown Inertial Navigation System (GNSS/SINS) and the Kalman filter is designed. Then the processing of ERAIM is presented, in which the least-squares theory is used to calculate the best estimators by integrating the predicted states with measurement states of Kalman filter. Based on the new measurement model, the integrity monitoring for GNSS/inertial system is carried out, including the fault detection, identification, reliability and separability. Lastly, the simulation and analysis for ERAIM vs RAIM are performed to validate the proposed method. Findings |!|#8211; Simulation results show that the ERAIM method is able to detect and identify effectively any type of failure including step failure and ramp failure. Compared to the RAIM method for only GNSS, the ERAIM increases the redundant information and reduces the correlation of test statistics, as well as enhancing the reliability and thus can significantly improve the performance of integrity monitoring. Practical implications |!|#8211; In safety critical sectors such as aviation, stringent integrity performance requirements must be met. The ERAIM method cannot only be used in integrity monitoring for the integrated GNSS/Inertial system, but also can be applied to only GNSS or other integrated navigation systems for general aviation aircraft. Originality/value |!|#8211; The paper presents a new integrity monitoring method of ERAIM, which is able to improve the fault detection and identification capabilities significantly by extending GNSS-used RAIM method into the GNSS/Inertial integrated system. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system. [ABSTRACT FROM AUTHOR]

Recent technological advances in both GPS and lowcost microelectromechanical-system (MEMS)-based inertial sensors have enabled the monitoring of the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. GPS is presently widely used in land vehicles. However, in some environments, the GPS signal may suffer from signal blockage and multipath effects that deteriorate the positioning accuracy. When miniaturized inside any moving platforms, the MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced inertial sensor system (RISS) with MEMS-based inertial sensors. In this paper, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle's odometer, and the whole system is integrated with GPS to obtain a 3-D navigation solution. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called Mixture PF. The performance of the proposed 3-D navigation solution using Mixture PF for RISS/GPS integration is examined by road-test trajectories in a land vehicle. The proposed method is compared with four other solutions: 1) 3-D solution using KF for full INS/GPS integration; 2) 2-D solution using KF for RISS/GPS integration; 3) 2-D solution using Mixture PF for RISS/GPS integration; and 4) 3-D solution using sampling/importance resampling (SIR) PF for RISS/GPS integration. The experimental results show that the proposed solution outperforms all the compared counterparts. [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]

Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter. [ABSTRACT FROM AUTHOR]

In this paper, a seamless navigation dual-model based on Square-Root Cubature Kalman Filter (SRCKF) and Random Forest Regression (RFR) is developed to enhance the performance of the Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation system. By using the proposed method, the system can ensure seamless navigation ability even during GPS signal outages. In the proposed dual-model, sub-model 1 that directly relates the specific force of INS to the measurement of filter and sub-model 2 that directly relates the cubature points and innovation of SRCKF to the error caused by filter are established. Combined with SRCKF and RFR algorithms, the dual-model system can predict and estimate the velocity and position of the vehicle seamlessly when GPS signals are blocked. Field test data are collected to evaluate the proposed solution, and the experimental results show that the model proposed has obvious improvement in navigation accuracy by comparison. The prominent advantages of the proposed seamless navigation method include the following: (i) the proposed dual-model can effectively provide corrections to standalone INS during GPS outages, which outperforms traditional widely used single model; (ii) the proposed combination of SRCKF and RFR achieves better performance in the prediction of INS errors than other combination algorithms. [ABSTRACT FROM AUTHOR]

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn’t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed, and a χ2-test is performed for this indicator. If it fails the test, the corresponding observation value is considered as an outlier. A scale factor is constructed according to this outlier, which is then used to lower the filter gain dynamically to decrease the influence of outliers. The simulation results demonstrate that the observation outlier processing method does not affect the normal values under normal circumstances; it can also discriminate between single and continuous outliers without errors or omissions. The impact time of outliers is greatly reduced, and the system performance is improved by more than 90%. Experimental results indicate that the proposed methods are effective in suppressing GNSS observation outliers in integrated navigation. [ABSTRACT FROM AUTHOR]

In the presence of global positioning system signal rejection, the low-cost inertial navigation system exhibits a significant cumulative error, leading to poor positioning accuracy of slave unmanned surface vehicles (SUSVs) in the cooperative navigation (CN) system for leader-slave. To improve the positioning accuracy of SUSVs, the extended Kalman filter (EKF) is augmented with distance information from external distance sensors. However, in the CN system of single leader unmanned surface vehicle, there may be a weaker observability, resulting in suboptimal estimated effects of the EKF. For the first time, the CN algorithm with observability and trilateral positioning method (TPM) is proposed to compensate the estimated position errors for EKF. An event trigger constructed from observability is used to assign the base stations and tags of the TPM. Then, the estimated positions of the tags are obtained by EKF when observability becomes weaker and the estimated positions are corrected by the measurements from TPM. Finally, more accurate estimated positions are obtained by weighted mean and the weights of compensation are automatically adjusted with observability. The results of the validation experiments and simulations show that the estimated positional accuracy of the proposed CN algorithm has improved by 40–50% compared to EKF. • Trilateral positioning method and observability are first offered to compensate the estimated position errors for EKF when observability becomes weaker in the cooperative navigation system of unmanned surface vehicles. • In order to improve the operational efficiency of the proposed collaborative navigation algorithm, event trigger is constructed based on observability to selectively compensate the estimation errors from the EKF. Finally, estimated positions with higher accuracy are achieved by obtaining automatically varying compensation weights through observability. • The experimental results and simulations show that the positional accuracy of the algorithm proposed in this paper is improved by 40–50% compared to EKF. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) is a widely used technology for high accuracy positioning in support of many applications, including airborne mapping and sensor georeferencing. The integration of GNSS and an Inertial Navigation System (INS) is a critical navigation component in an airborne mapping system, because it can provide high update rate of solutions, platform attitude information and improved performance (e.g. accuracy, availability). However, GNSS signals can be easily blocked or interfered with, and sometimes the satellite geometry may not be good enough for high accuracy and high reliability applications. Therefore the development of alternative, non-GNSS-based navigation solutions has attracted increased interest in situations where GNSS system availability cannot be guaranteed. Locata is a groundbased navigation system which can transmit ranging signals at several frequencies in the 2.4 GHz Industrial, Scientific and Medical radio bands. Tracking of Locata signals allows additional range-like measurements to be made, which can be processed in a manner not unlike GNSS measurements. Hence the integration of GNSS, INS and Locata can address requirements for seamless precise positioning in many situations. This paper describes a flight test of a GPS+INS+Locata triple-integrated system. The test was conducted in October 2011, during a flight from Bankstown Airport, Sydney, to Cooma Snowy Mountains Airport. The Locata pseudorange measurements were collected and postprocessed to evaluate overall navigation performance and to investigate the limitations of such an integrated system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of proper integration of an inertial navigation system with an air data system. First, we present a mathematical formalism of the system's mechanization, i.e., navigation equations for spheroidal Earth, air data observation equations, and error propagation equations for optimal Kalman filtering. Next, an analytical observability analysis is conducted and demonstrated by means of a simulation. Finally, we draw conclusions about expected benefits and the most suitable applications. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

This paper deals with the issue of incorporating pseudolite measurements into an integrated Global Positioning System/Inertial Navigation System (GPS/INS) positioning and attitude system with a view to improving signal availability, solution reliability, and accuracy in a localized area. Existing GPS/INS systems can overcome inherent shortcomings of each of the navigation technologies (line-of-sight signal requirement for GPS and INS errors that grow with time); therefore, such systems are now used for a wide variety of land, sea, and airborne applications where accurate positioning and/or attitude information is required with high output rate. However, their performance can still be degraded under certain conditions, such as when the duration of satellite signal blockage exceeds a certain time period (related to the quality of the INS), resulting in large accumulated INS errors. Such a scenario is a common occurrence for many kinematic applications. In an integrated GPS/Pseudolite/INS scheme, in order to gain the maximum benefit from additional pseudolite measurements, it is necessary to investigate how pseudolites can best be deployed to complement an existing GPS/INS system. A series of simulations, as well as field experiments with a GPS/Pseudolite/INS system comprising a NovAtel Millennium GPS receiver, an IntegriNautics IN200 pseudolite, and a MIGITS strapdown INS, were carried out, and the impact on performance of integrating pseudolite(s) has been assessed for a variety of operational conditions and different system configurations. The results indicate that the overall performance of the system can indeed be significantly improved using additional pseudolite measurements. [ABSTRACT FROM AUTHOR]

Introduction Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. Methods The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based [ABSTRACT FROM AUTHOR]

Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No GNSS signals. An improved LSTM (long short-term memory) neural network in No GNSS signal conditions is proposed to assist the combination of navigation data and the positioning algorithm. When the GNSS signal is normal input, the current on-board combination of the navigation module's output sensor data information is used for training to improve the LSTM algorithm and to establish the incremental output of the GNSS position of the mapping of the different weights. In No GNSS signal conditions, using the improved LSTM algorithm can improve the combination of navigation and positioning algorithms. Under No GNSS signal conditions, the improved LSTM training model is used to predict the dynamics of SINS information component data. Under No GNSS signal conditions, the combined navigation filtering design is completed, and the error correction of SINS navigation and positioning information is carried out to obtain a more accurate combination of navigation and positioning system accuracy. It can be seen through the actual test experiment using a sports car in the two trajectories under the conditions of No GNSS signals that the proposed algorithm can be compared with the LSTM algorithm. In testing road sections, the proposed algorithm, when compared with the LSTM algorithm to obtain the northward position that the mean square errors were improved by 55.63% and 76.64%, and the eastward position mean square errors were improved by 43.42% and 54.67%. In a straight-line trajectory, improving the effect's navigation and positioning accuracy and reliability is significant. [ABSTRACT FROM AUTHOR]

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported. [ABSTRACT FROM AUTHOR]

This paper presents a high-accuracy, multirate inertial navigation system (INS) integrating global positioning system (GPS) measurements and advanced vector aiding techniques for precise position and attitude estimation of autonomous surface crafts (ASCs). Designed to be implemented and tested in the DELFIMx catamaran developed at ISR/IST, the navigation system comprises an advanced inertial integration algorithm to account for coning and sculling motions, combined with an extended Kalman filter (EKF) for inertial sensor error compensation. Aiding gravitational observations are optimally exploited in the EKF, by deriving a sensor integration technique that takes into account the vehicle's dynamics bandwidth information to properly trace measurement disturbances and extract the relevant sensor information. The proposed aiding technique and the performance of the navigation system are assessed using experimental data obtained at seatrials with a low-cost hardware architecture installed on-board the DELFIMx platform. It is shown that the low frequency information embodied in pendular measurements improves the compensation of inertial sensor bias and noise, and consequently enhances the performance of position and attitude estimation. The overall improvements obtained with the vector aiding observations are also illustrated for the case of GPS signal outage, emphasizing the extended autonomy of the navigation system with respect to position aiding. [ABSTRACT FROM PUBLISHER]

Navigational sensors are evolving both on a commercial and research level. However, the limitation still lies in the accuracy of the respective sensors. For a navigation system to reach a certain accuracy, multi sensors or fusion sensors are used. In this paper, a framework of fuzzy sensor data fusing is proposed to obtain an optimised navigational system. Different types of sensors without a known state of inaccuracy can be fused using the same method proposed. This is demonstrated by fusing compass/accelerometer and GPS signal. GPS has evolved as a choice of navigation method for outdoor autonomous system. Despite it emerging trend and application, one problem remains is that it is still prone to inaccuracies due to environmental factors. These factors are available and evaluated in the GPS receiver architecture. These inaccuracies are available in the extracted NMEA(National Maritime Electronics Association) protocols as SNR (Signal to noise ratio) and HDOP (Horizontal Dillusion of Precision). Dead reckoning sensors on the other hand does not depend on external radio signal coverage and can be used in areas with low coverage. Unfortunately, the errors are unbounded and has an accumulative effect over time. [ABSTRACT FROM AUTHOR]

GPS is the most widely used global navigation satellite system. By design, there is no provision for real time integrity information within the Standard Positioning Service (SPS). However, in safety critical sectors like aviation, stringent integrity performance requirements must be met. This can be achieved externally or at the receiver level through receiver autonomous integrity monitoring (RAIM). The latter is a cost effective method that relies on data consistency, and therefore requires redundant measurements. An external aid to provide this redundancy can be in the form of an Inertial Navigation System (INS). This should enable continued performance even during RAIM holes (when no redundant satellite measurements are available). However, due to the inclusion of an additional system and the coupling mechanism, integrity issues become more challenging. To develop an effective integrity monitoring capability, a good understanding of the potential failure modes of the integrated system is vital. In this paper potential failure modes of integrated GPS/INS systems are identified. This is followed by the specification of corresponding models that would be required to investigate the capability of existing integrity algorithms and to develop enhancements or new algorithms. [ABSTRACT FROM AUTHOR]

Temporary degraded GPS (DGPS) position loss, in circumstances such as an overhead bridge, can be alleviated by an inertial navigation system (INS) that uses onboard sensors, such as yaw and speed sensors, to determine vehicle position. This paper introduces a post-processing DGPS/INS integration approach based on using the INS solution during DGPS outages or periods of low accuracy DGPS position solutions. In this approach, the INS solution initialization is performed using the DGPS solution before DGPS position solution loss, and measurements from the Inertial Measurement Unit (IMU). The final post-processed INS solution is a weighted average of the INS forward and backward solutions. This work constitutes two parts: the INS initialization methods for different degrees of freedom vehicle positioning models, and the developed weighting model necessary to combine the forward and the backward solutions. The former part is essential in obtaining acceptable INS initial states for both the stand-alone INS or any post-processing or real time INS/GPS integrated system. The latter part is based on the use of the complementary error behaviours of the backward and the forward solutions, and can be used as a survey method with acceptable position solutions accuracies. Applying the forward/backward INS combined solution method on real data shows that the resultant INS solution accuracy is 35 cm or less over a 1000 m road segment. This method is used to survey freeways interchange road segments where 50% of the surveyed distance has no DGPS solution or has a degraded DGPS solution. The average achieved accuracy over the whole freeways interchange is around 40 cm over a 23 km distance. [ABSTRACT FROM AUTHOR]

With the rapid development of autopilot technology, a variety of engineering applications require higher and higher requirements for navigation and positioning accuracy, as well as the error range should reach centimeter level. Single navigation systems such as the inertial navigation system (INS) and the global navigation satellite system (GNSS) cannot meet the navigation requirements in many cases of high mobility and complex environments. For the purpose of improving the accuracy of INS-GNSS integrated navigation system, an INS-GNSS integrated navigation algorithm based on TransGAN is proposed. First of all, the GNSS data in the actual test process is applied to establish the data set. Secondly, the generator and discriminator are constructed. Borrowing the model structure of generator transformer, the generator is constructed by multilayer transformer encoder, which can obtain a wider data perception ability. The generator and discriminator are trained and optimized by the production countermeasure network, so as to realize the speed and position error compensation of INS. Consequently, when GNSS works normally, TransGAN is trained into a high-precision prediction model using INS-GNSS data. The trained Trans- GAN model is emoloyed to compensate the speed and position errors for INS. Through the test analysis of flight test data, the test results are compared with the performance of traditional multi-layer perceptron (MLP) and fuzzy wavelet neural network (WNN), demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted, with the high accuracy. [ABSTRACT FROM AUTHOR]

An integrated navigation method based on INS and GPS was proposed for airborne navigation. The influence of scale factor error and misalignment error of gyroscope and accelerometer on navigation accuracy was analyzed. Compared with traditional INS/GPS integrated navigation method, scale factor error and misalignment error were added to the state model of the integrated navigation system. The observability of scale factor error and misalignment error was analyzed combined with typical airborne movement. Then the integrated system was optimized, and the new navigation model of the integrated system was obtained. The optimized INS/GPS integrated model was validated by numerical simulation and turntable test. Comparing the proposed model with traditional integrated model (integrated system error states do not include scale factor error and misalignment error), the results showed that the proposed integrated navigation method can improve the accuracy from 8% to 28% of the east, north, and upward positions. [ABSTRACT FROM AUTHOR]

Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method's performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability. [ABSTRACT FROM AUTHOR]

In response to the lack of specific demonstration and analysis of the research on the necessity of the Lie group strapdown inertial integrated navigation error model based on the Euler angle, two common integrated navigation systems, strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) and strapdown inertial navigation system/doppler velocity log (SINS/DVL), are used as subjects, and the piecewise constant system (PWCS) matrix, based on the Lie group error model, is established. From three aspects of variance estimation, the observability and performance of the system with large misalignment angles for low, medium, and high accuracy levels, traditional error model, Lie group left error model, and right error model are compared. The necessity of research on Lie group error model is analyzed quantitatively and qualitatively. The experimental results show that Lie group error model has better stability of variance estimation, estimation accuracy, and observability than traditional error model, as well as higher practical value. [ABSTRACT FROM AUTHOR]

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters. [ABSTRACT FROM AUTHOR]

The last decade has witnessed a wide spread of small drones in many civil and military applications. With the massive advancement in the manufacture of small and lightweight Inertial Navigation System (INS), navigation in challenging environments became feasible. Navigation of these small drones mainly depends on the integration of Global Navigation Satellite Systems (GNSS) and INS. However, the navigation performance of these small drones deteriorates quickly when the GNSS signals are lost, due to accumulated errors of the low-cost INS that is typically used in these drones. During GNSS signal outages, another aiding sensor is required to bound the drift exhibited by the INS. Before adding any additional sensor on-board the drones, there are some limitations that must be taken into considerations. These limitations include limited availability of power, space, weight, and size. This paper presents a novel unconventional method, to enhance the navigation of autonomous drones in GNSS denied environment, through a new utilization of hall effect sensor to act as flying odometer "Air-Odo" and vehicle dynamic model (VDM) for heading estimation. The proposed approach enhances the navigational solution by estimating the unmanned aerial vehicle (UAV) velocity, and heading and fusing these measurements in the Extended Kalman Filter (EKF) of the integrated system. [ABSTRACT FROM AUTHOR]

In the exploration of polar regions, navigation is one of the most important issues to be resolved. To avoid the limitations of a single navigation coordinate frame, navigation systems usually use different navigation coordinate frames in polar and nonpolar regions, such as the north-oriented geographic frame and the grid frame. However, the error states and covariance matrix are related to the definition of the navigation coordinate frame, since coordinate frame conversion will cause overshoot of the integrated navigation Kalman filter and error discontinuity. To solve this problem, the transformation relationship of error states defined in different frames is deduced, whereby the covariance matrix transformation relationship is also analyzed. On this basis, covariance transformation-based open-loop and closed-loop Kalman filter integrated navigation algorithms are proposed. The effectiveness of the algorithms is verified by flight tests with a rotational strapdown inertial navigation system/global navigation satellite system integrated navigation system. [ABSTRACT FROM AUTHOR]

Resilient navigation in Global Navigation Satellite System (GNSS)-degraded and -denied environments is becoming more and more required for many applications. It can typically be based on multi-sensor data fusion that relies on alternative technologies to GNSS. In this work, we studied the potential of a low earth orbit (LEO) satellite communication system for a high-dynamic application, when it is integrated with an inertial measurement unit (IMU) and magnetometers. We derived the influence of the main error sources that affect the LEO space vehicle (SV) Doppler-based navigation on both positioning and attitude estimations. This allowed us to determine the best, intermediate and worst cases of navigation performances. We show that while the positioning error is large due to large orbit errors or high SV clock drifts, it becomes competitive with that of an inertial navigation system (INS) based on a better quality IMU if precise satellite orbits are available. On the other hand, the attitude estimation tolerates large orbit errors and high SV clock drifts. The obtained results suggest that LEO SV signals, used as signals of opportunity for navigation, are an attractive alternative in GNSS-denied environments for high dynamic vehicles. [ABSTRACT FROM AUTHOR]

Navigation of gun-launched precision munitions using affordable technologies is investigated. Estimation algorithms were developed to blend flight dynamic models with measurements from inertial sensors. The launch and flight characteristics of the unique, gun-launched environment were exploited both in the state estimator and for novel heuristic parameter identification. Using the heuristic parameter, the inertial navigation system was calibrated without a global positioning system (GPS), and its performance was compared with a similar calibration performed with a GPS. The experimental results from guided mortar flights indicate that the algorithm with only inertial sensor measurements yields position errors of less than 40 m over a 30-s flight. Trade studies were conducted in simulation to assess algorithm performance over a wider range of conditions such as variation in the flight dynamic model parameters. These results demonstrate that position errors are less than tens of meters for flight times of interest to munitions. Estimation is tolerant to inertial sensor errors because of the novel manner in which known flight dynamics are used to compensate measurements. Overall, this effort shows that navigation error resulting from a low-throughput algorithm using affordable inertial sensors is sufficient to increase system accuracy for munitions in GPS-denied environments. [ABSTRACT FROM AUTHOR]

Abstract: The concept and results of integration of a strap-down inertial navigation system (INS) based on low-accuracy inertial sensors and the global positioning system (GPS) have been presented in this paper. This system is aimed for the purposes of navigation, automatic control, and remote tracking of land vehicles. The integration is made by the implementation of an extended Kalman filter (EKF) scheme for both the initial alignment and navigation phases. Traditional integration schemes (centralized and cascaded) are dominantly based on the usage of high-accuracy inertial sensors. The idea behind the suggested algorithm is to use low-accuracy inertial sensors and the GPS as the main source of navigation information, while the acceptable accuracy of INS is achieved by the proper damping of INS errors. The main advantage of integration consists in the availability of reliable navigation parameters during the intervals of absence of GPS data. The influence of INS error damping coefficients is different depending on the fact whether the moving object is maneuvering or is moving with a constant velocity at that time. It is proposed that INS error damping gain coefficients generally should take higher values always when GPS data are absent, while at the same time their values in the error model (EKF prediction phase) can be additionally adapted according to the actual values of vehicle acceleration. The analysis of integrated navigation system performances is made experimentally. The data are acquired along the real land vehicle’s trajectory while the intervals of absence of GPS data are introduced artificially on the parts characterized both by maneuver and by constant velocity. [ABSTRACT FROM AUTHOR]

Navigation systems of safety‐critical unmanned aircraft need an alternative position aiding source to global navigation satellite system. One promising alternative, is position measurements from phased‐array radio systems. The presented navigation system consist of a multiplicative extended Kalman filter that utilize these measurements, along with an exogenous altitude measurement, to aid an inertial navigation system. The reliability and accuracy of the navigation system is demonstrated through beyond‐visual‐line‐of‐sight flights with a fixed‐wing unmanned aerial vehicle, without the use of global navigation satellite systems. This uses a conventional guidance controller module, which also monitor the navigation quality. [ABSTRACT FROM AUTHOR]