Robust adaptive filtering for extended target tracking with heavy‐tailed noise in clutter

Abstract

A robust adaptive filter is proposed by using the variational Bayesian (VB) inference to extended target tracking with heavy-tailed noise in clutter. An explicit distribution is used to describe the non-Gaussian heavy-tailed noise based on Student's t -distribution. The need for arbitrary decisions is then eliminated, and the robust operation is provided which is less sensitive to extreme observation. Moreover, an approximate measurement update using the analytical techniques of VB methods is derived to approximate the posterior states at each time step. To obtain a more accurate result, clutter estimation is also integrated considering the uncertainty of target tracking in a cluttered environment. The performance of the proposed algorithm is demonstrated with simulated data.