A Heavy-Tailed Noise Tolerant Labeled Multi-Bernoulli Filter

Abstract

The well-known labeled multi-Bernoulli (LMB) filter for multi-target tracking in clutters works well only under the Gaussian noise assumptions. Since this Gaussian assumption can hardly hold in practice, we present the problem of the LMB with heavy-tailed non-Gaussian measurement noise. Through modeling the measurement noise as Student's t distribution, a heavy-tailed measurement noise tolerant LMB (TLMB) is derived in the framework of variational Bayesian inference for the joint estimation of the target state together with the unknown scale matrix and degree of freedom (dof) of the Student's t distribution. Simulations on multi-target tracking in clutter with unreliable sensor demonstrate the effectiveness and superiority of the proposed TLMB.