An Improved Kalman Filter Based on Self-adaptive Adjustment Parameters

This paper considers an improved Kalman filter (KF) for a non-Gaussian system, when an adaptive statistics model is applied to capture the systematic characteristics in real time. The problem is formulated as self-adaptive adjustment parameters (SAPs) updating by the recursive least squares (RLS) algorithm. These parameters are shown to admit adaptive statistics model to characteristics of which applies and extends results given earlier in “Online denoising based on the second-order adaptive statistics model” (S. L. Yi and X. B. Jin et al., Sensors, 17(7), 1668, 2017.). Simulations comparing the improved KF based on the SAPs to the standard KF and the past algorithm illustrate a satisfactory performance when applied to self-adaptive adjustment parameters. Simulation results show that the proposed algorithm can gradually converge with a small performance loss.