A robust fast LMB filter for superpositional sensors

Abstract

This paper aims to deal with the problem of multitarget tracking based on superpositional sensors, while the measurement noise is unknown. To this end, the labeled multi-Bernoulli (LMB) filter is adopted in order to jointly estimate the number of existing targets and the state of each target recursively. Since the LMB filter admits no analytical solution, a Gaussian mixture (GM) approximated implementation is proposed. In order to deal with the unknown measurement noise, the single target state is augmented to incorporate the noise parameters, and the augmented state is modeled as the Gaussian and inverse Wishart mixture (GIWM) distribution. The posterior is approximately computed using the variational Bayesian (VB) estimation method so as to ensure that the updated distributions of augmented states belong to the same family of the predicted one, thus can be used as prior for the subsequent iterations. The performance of the proposed algorithm is assessed via simulation experiments.