An Adaptive Strong Tracking Cubature Kalman Filter with Unknown Measurement Noise Covariance and Its Application

Abstract

Strong tracking Kalman filter is proposed to address the performance degradation and divergence caused by process model uncertainty. However, strong tracking filters depend on prior information about the measurement noise, which is frequently unknown or time-varying in real-world applications. This paper proposed an adaptive strong tracking cubature Kalman filtering algorithm with unknown measurement noise covariance. First, we introduce a modified pseudo-measurement-based covariance estimation method. It estimates measurement covariance by calculating the second-order mutual difference between the measurement sequence and pseudo measurement sequence. Second, we propose a filter divergence detecting method to help decide when to adjust the prediction error covariance matrix. The accuracy of the measurement noise covariance matrix estimating method and the effectiveness of filter divergence detecting methods have been proved by simulation results, respectively. As a result, the proposed filter outperforms several other algorithms in precision or robustness with inaccurate measurement noise covariance.