PETC-EM-PMBM Filter for Tracking Point and Extended Targets With Type Conversion

Abstract

This article proposes a filter based on the Poisson multi-Bernoulli mixture (PMBM) distribution for tracking point and extended targets with type conversion (PETC). First, we design a generalized measurement model based on the fixed measurement density. The model attributes target type conversion to changes in the projected extension of the target. Second, we systematically derive the PMBM recursion process in a single-target hybrid space, which allows the Gaussian density and the Gamma Gaussian inverse Wishart (GGIW) density to propagate simultaneously. We design a density transition model to capture possible conversions between these two densities. In addition, we create an expectation-maximization (EM) method to improve the estimation accuracy of the density transition probability. Lastly, a more efficient PMB approximation to the PMBM is derived. Simulation experiments demonstrate the superiority of our filters in maintaining continuous tracking when the target type is converted.