Abstract Aiming at the problems existing in the practical application of federated filter and centralized Kalman filter as well as the problems of decreased filtering accuracy due to inaccurate statistical characteristics of measurement noise in integrated navigation, we propose an adaptive distributed filtering method of multi-sensor integrated navigation system using variational Bayesian estimation and distributed estimation. Firstly, based on the centralized Kalman filter, we derive an expression distributed filtering method which has the characteristics of optimal estimation, automatic fault isolation and no pollution to the overall navigation system. Then we propose an adaptive distributed filtering method based on variational Bayesian estimation technique, which we use to estimate the system state and time-varying measurement noise variances synchronously and in real time. Finally, we use the SINS/GNSS/CNS/ADS integrated navigation system for simulation verification. The simulation results show that this algorithm can track the abruptly or slowly varying variance of the measurement noise in real time, can effectively improve the filtering accuracy of the overall integrated navigation system and reduce the adverse effects of inaccurate statistical characteristics of the measurement noise compared with the federated filter algorithm.
WANG Wei,SUN Weiwei,PAN Xinlong et al. An Adaptive Distributed Filtering Algorithm for Multi-Sensor Integrated Navigation System[J]. jgg, 2023, 43(12): 1275-1280.
WANG Wei,SUN Weiwei,PAN Xinlong et al. An Adaptive Distributed Filtering Algorithm for Multi-Sensor Integrated Navigation System[J]. jgg, 2023, 43(12): 1275-1280.