Process noise estimator based on observation sequence and its application on inertial navigation system

Abstract

Kalman filter has been extensively applied in vast areas. However, it is widely acknowledged that the performance of Kalman filter depends on the accuracy of priori information such as model structure, statistics information of process and observation noise. Obtaining the covariance matrix of process noise is difficult in some application scenarios. Considering such background, this paper presents a process noise estimation algorithm based on the noise observation sequence. By constructing a transform matrix and removing the state variables from the observation, the noise observation sequence can be established, through which the covariance matrix of process noise can be estimated. Comparing to conventional adaptive filter, this algorithm needs less calculation. Moreover, the noise estimation process is separated from Kalman filter thus ensures Kalman Filters independence and optimality. The simulation results show that the new algorithm can effectively estimate the process noise covariance, and remain uninfluenced by the initial condition.