Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

Abstract

A new variational Bayesian-based adaptive distributed fusion unscented Kalman filter (ADFUKF-VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses. First, the Bernoulli random variables are introduced as the judgement factors of the measurement losses in each local unscented Kalman filter (UKF) of the distributed fusion target tracking system, and the probability density function (PDF) of the measurement loss probability is modelled as a Beta distribution. Then, the variational Bayesian (VB) approach is used to infer the estimated variables in each local UKF. The measurement losses are judged according to the expectations of the Bernoulli random variables, and three ways are adopted to estimate the state vector based on the obtained judgement results. Finally, the covariance intersection (CI) fusion strategy is used to achieve the fusion of all local state estimates. The simulation results show that the proposed ADFUKF-VB algorithm can accurately judge whether the measurement is lost and estimate the unknown measurement loss probability, moreover, effectively improve the accuracy of state estimation compared with the existing filtering algorithms with unknown measurement losses.