The Labeled Multi-Bernoulli Filter for Multitarget Tracking With Glint Noise

A labeled multi-Bernoulli (LMB) filter is presented to perform multitarget tracking (MTT) for the glint noise. The measurement noise is modeled as a multivariate Student- $t$ process. The variational Bayesian method is applied in the LMB framework with the augmented state. The predictive likelihood is calculated via minimizing the Kullback–Leibler divergence by the variational lower bound. Simulation results show that our approach is effective in MTT with the glint noise.