Multi-Extended Target Tracking Algorithm Based on VBEM-CPHD

Abstract

Considering that the extended targets tracking problem of the measurement is a glint noise with an unknown inverse covariance, a new algorithm from a multi-extended target tracking based on the Variational Bayesian Expectation Maximization (VBEM) is proposed. To improve the variational Bayesian technique, a modeled Student’s t distribution is proposed based on multiple Gaussian mixture terms in order to replace the probability hypothesis density (PHD) intensity. The extended target was modeled with a Student’s t distribution with the glint noise, using Gauss-Gamma distribution combining the variational Bayesian technique to obtain an approximate distribution and applying the expectation-maximization algorithm for iterative estimation. The two experiments are compared and analyzed with the VBEM-CPHD algorithm and the traditional extended target tracking algorithm. Experiment 1 estimated the trajectory of the target, compared algorithms of VBEM-CPHD and GM-CPHD with OSPA distance, varied glint noise with the three different measurement noise standard deviations. Experiment 2 completed the examination of the tracking performance and stability of the proposed method and performed to compare the VBEM-CPHD algorithm proposed with the VB-based GM-CPHD (GM-VBCPHD) algorithm under an unknown measurement noise covariance. The experimental results indicate that the algorithm of VBEM-CPHD has high tracking accuracy, good adaptability, and a strong antijamming ability for multiple extended targets under glint noise conditions.