Tightly coupled distributed Kalman filter under non-Gaussian noises

Abstract

Distributed Kalman filter (DKF) is an effective method to solve the problem of state estimation in multi-sensor systems. However, the accuracy of conventional DKF may be influenced by the non-Gaussian noises and the accumulated errors in local Kalman filters (LKFs). To encounter the above challenges, a tightly coupled DKF with covariance intersection is proposed. In our method, using the computation outputs of LKFs, a Gaussian Mixture Model is formulated to address the influence of non-Gaussian noises. In order to reduce the cumulative errors from LKFs, covariance intersection fusion is utilized. Furthermore, an index-Huber function is designed to reduce the impact of large covariance generated by the LKFs. Several simulation and real-world experiments are conducted to show the effectiveness of our methods. The method we proposed outperforms three other DKF algorithms in the metrics of RMSE and cumulative error. In addition, a real-world multi-sensor state estimation experiment is conducted on a hexapod robot.