A Variational Bayesian Labeled Multi-Bernoulli Filter for Tracking with Inverse Wishart Distribution

In multi-target tracking (MTT), the imprecise model for sensor characteristics might result in poor performance. The Variational Bayesian labeled multi-Bernoulli (VB-LMB) filter based on Gamma distribution can handle this problem. However, the predictive likelihood of the existing VB-LMB filter is simply treated as a Gaussian, which is inaccurate. In this paper, a VB-LMB filter with inverse Wishart distribution is presented to perform MTT under the unknown sensor characteristics. The measurement noise covariance is modeled as an inverse Wishart (IW) distribution. This distribution has potential to deal with the full noise covariance matrix compared with the Gamma distribution. Since the state and the measurement noise covariance are coupled, the updated equation can be solved by variational Bayesian (VB) method. The predictive likelihood is calculated via minimizing the Kullback-Leibler divergence by the VB lower bound. A MTT scenario is used to evaluate the proposed method. Simulation results show that our approach has better performance than the existing VB-LMB filter with the Gamma distribution.