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.