Random Weighting-Based Nonlinear Gaussian Filtering
Zhaohui Gao, Chengfan Gu, Jiahui Yang, Shesheng Gao, Yongmin Zhong
Abstract
The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy.