A Robust Student's t-Based Labeled Multi-Bernoulli Filter

The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student's t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student's t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student's t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.