To improve the ability of autonomous navigation for Unmanned Surface Vehicles (USVs), multi-sensor integrated navigation based on Inertial Navigation System (INS), Celestial Navigation System (CNS) and Doppler Velocity Log (DVL) is proposed. The CNS position and the DVL velocity are introduced as the reference information to correct the INS divergence error. The autonomy of the integrated system based on INS/CNS/DVL is much better compared with the integration based on INS/GNSS alone. However, the accuracy of DVL velocity and CNS position are decreased by the measurement noise of DVL and bad weather, respectively. Hence, the INS divergence error cannot be estimated and corrected by the reference information. To resolve the problem, the Adaptive Information Sharing Factor Federated Filter (AISFF) is introduced to fuse data. The information sharing factor of the Federated Filter is adaptively adjusted to maintaining multiple component solutions usable as back-ups, which can improve the reliability of overall system. The effectiveness of this approach is demonstrated by simulation and experiment, the results show that for the INS/CNS/DVL integrated system, when the DVL velocity accuracy is decreased and the CNS cannot work under bad weather conditions, the INS/CNS/DVL integrated system can operate stably based on the AISFF method.

Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method.

Accurate heading is one of the critical issues in vehicle navigation. To achieve accurate and stable navigation in all weather conditions, a combination of local sensors, such as gyroscopes, and global sensors, such as magnetometers, is typically used to fuse information. This information fusion enables the system to achieve both local accuracy and global, drift-free heading estimation during long-term navigation. The sensitivity of magnetometers to various coupled magnetic interferences often leads to a decrease in the accuracy of heading estimation. To enhance the system’s anti-interference ability, this article proposes a method for magnetometer/gyroscope integration based on random magnetic interference detection. First, random magnetic interference is separated from constant magnetic interference, and a linear model using redundant information from magnetometer/gyroscope is established, which can be processed by Pearson correlation coefficient (PCC) to complete random magnetic interference detection across all motion states. Then, random magnetic interference data are eliminated to improve constant magnetic interference calibration based on rectangular path. Finally, adaptive Kalman filtering (AKF) is implemented based on the random magnetic interference detection results to improve the accuracy of magnetometer/gyroscope heading estimation. The effectiveness of the proposed algorithm is verified by actual test.

When employing gyroscope/magnetometer integration for vehicle heading estimation, the challenge of timevarying random magnetic interference in the environment frequently arises, resulting in reduced accuracy in heading estimation. To address this issue, this paper presents an approach to enhance vehicle heading estimation based on adaptive sliding window Factor Graph Optimization (FGO) for gyroscope/magnetometer integration. The method calculates the magnetometer angular velocity by taking the differential of the magnetometer heading. It introduces the difference between the magnetometer and gyroscope angular velocities as a feature to detect random magnetic interference, and applies variance threshold optimization to adaptively adjust the sliding window length. This ultimately achieves an optimal heading estimation within the sliding window. Experimental results demonstrate the effectiveness of the proposed algorithm in improving the accuracy of vehicle heading estimation in complex random magnetic interference environments.

In inertial measurement unit (IMU)/magnetometer integrated pedestrian positioning systems, the absolute heading from the magnetometer is severely degraded by pervasive magnetic interference, introducing uncertainty into the integrated solution and reducing overall positioning accuracy. Conventional ellipsoid-fitting methods for magnetometer calibration typically rely on minimizing the algebraic distance; however, they perform poorly under highly inhomogeneous data distributions. Moreover, the unknown relationship between heading-error statistics and magnetometer orientation in 3-D space can significantly degrade system performance. Addressing these challenges, this study proposes a data-driven progressive ellipsoid-fitting method to mitigate deterministic magnetic interference. In parallel, an adaptive extended Kalman filter (EKF) enhanced by a multilayer perceptron (MLP) models the statistical characteristics of heading errors in various spatial orientations. By dynamically adjusting the observation noise matrix in real time, the system effectively suppresses stochastic magnetic interference. This approach is especially suitable for foot-mounted pedestrian positioning, which typically exhibits limited motion degrees of freedom and only small rotational movements during normal use. Furthermore, it automatically estimates the observation noise level of IMU/magnetometer integrated positioning systems without any prior knowledge. The experimental results confirm that the proposed method delivers notable effectiveness in real-world scenarios.