A novel adaptive federated filter for GNSS/INS/VO integrated navigation system

Abstract

In order to solve the problem of decreased navigation performance of the Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation systems in GNSS-denied environments, and to improve the navigation accuracy and robustness of the navigation system, a novel adaptive federated filter with a feedback scheme for a GNSS/INS/visual odometry (VO) integrated navigation system is proposed in this paper. A visual-inertial odometry system model with a multi-state constraint Kalman filter structure based on a polar geometry and trifocal tensor geometry between different images is established, which can provide better navigation accuracy in GNSS-denied environments. Moreover, a new method to obtain the information allocation factor according to the different navigation performances of local filters is deduced in this paper, which has low computational complexity and a simple structure. Meanwhile, an abnormal measurement detection algorithm based on fuzzy logic is proposed to detect the abnormal measurements of local filters. The results of the vehicle experiment with the publicly available real-world KITTI dataset show that the proposed algorithm can obtain reliable navigation results in GNSS-denied environments and improve the navigation accuracy and robustness of the GNSS/INS/VO integrated navigation system.

A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter

“…Transfer alignment [18] divided the high-dimensional state vector into two parts Improved covariance [19] derived a real-time estimates of improved covariance SINS/GPS/CNS/Radar integrated system [11] calculated the state parameters with dual-state detection Joint filter to fuse data [20] INS/CNS/DVL combined system Federated unscented Kalman filter [21] with different vehicle motion models to estimate Federated hybrid filter [22] utilizes a minimum variance criterion to fuse An adaptive filter [23] conquer the performance degradation Federated filter with a feedback scheme [24] GNSS/INS/visual odometry combined positioning system Federated Kalman filter for indoor positioning distance is estimated through RSS and improved the calculation speed. Ma et al presented a federated adaptive filter based on improved covariance [19], which derived real-time estimates of improved covariance according to maximum likelihood estimation criteria.…”

“…An adaptive filter was utilized as a local filter, which can conquer the performance degradation. To enhance the accuracy and robustness, Yue Z et al proposed a federated filter with a feedback scheme for a GNSS/INS/visual odometry combined positioning system [24]. In [25], a federated Kalman filter (FKF) was applied to indoor positioning.…”

“…We update the filter equation in ( 25): (24) For the stability and robustness of the positioning system, the filtering gain K n should increase to track the rapid changing of motion and the system state. When the state is stable, the filtering gain can decrease to K g .…”

A SINS/DVL/USBL Integrated Navigation and Positioning IoT System with Multiple Sources Fusion and Federated Kalman Filter

“…Reference [24] presented a federated filter for multiple-sensor crosscorrelations strategy. An adaptive filter was utilize as a local filter, which can conquer the performance degradation .To enhance the accuracy and robustness, reference [25] proposed a federated filter with a feedback scheme for a GNSS/INS/visual odometry combined positioning system. In reference [26], a federated Kalman filter (FKF) was applied to indoor positioning.…”

In-Flight Alignment Method of SINS/GPS Integrated Navigation System Based on IUKF

“…In [13], a class-based weighted adaptive filtering algorithm was proposed to address the degradation of model uncertainty and observation anomaly in integrated navigation system, which resulted in the decline of accuracy and stability. In [14], an adaptive federated filter was proposed to obtain distribution factors based on navigation performance and detect abnormal measured values of the filter. Jiang et al proposed an adaptive multi-fading factors estimation algorithm for GPS/INS integrated navigation system when discussing model error and uncertainty of kalman filtering with good stability and robustness [15].…”