A novel robust Kalman filter with adaptive estimation of the unknown time-varying latency probability

Abstract

To better model one-step randomly delayed measurements (ORDM) with unknown time-varying latency probability (UTLP) in linear systems with heavy-tailed measurement noise (HMN), a novel Normal-Gamma-Beta mixture (NGBM) distribution is presented. By introducing a Bernoulli random variable, the probability density function of the proposed NGBM distribution can be reformulated as a Gaussian hierarchical form. Based on this, a novel robust Kalman filter is designed using the variational Bayesian technique. A target tracking simulation verifies the potential of the proposed robust filter, which has higher filtering accuracy than existing cutting-edge filters and can adaptively estimate the UTLP. Furthermore, it is concluded that when HMN and ORDM concurrently exist, the HMN has more influence on the accuracy of the filter than the ORDM.