Single Maneuvering Target Tracking in Clutter Based on Multiple Model Algorithm with Gaussian Mixture Reduction

Abstract

The measurement origin uncertainty and target (dynamic or/and measurement) model uncertainty are twofundamental problems in maneuvering target tracking in clutter. The multiple hypothesis tracker (MHT) and multiplemodel (MM) algorithm are two well-known methods dealing with these two problems, respectively. In this work, weaddress the problem of single maneuvering target tracking in clutter by combing MHT and MM based on the Gaussianmixture reduction (GMR). Different ways of combinations of MHT and MM for this purpose were available in previousstudies, but in heuristic manners. The GMR is adopted because it provides a theoretically appealing way to reducethe exponentially increasing numbers of measurement association possibilities and target model trajectories. Thesuperior performance of our method, comparing with the existing IMM+PDA and IMM+MHT algorithms, isdemonstrated by the results of Monte Carlo simulation.