Tracking of multiple maneuvering targets using multiscan JPDA and IMM filtering

Abstract

We consider the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and joint probabilistic data association (JPDA) technique. But unlike the standard single scan JPDA approach, we exploit a multiscan joint probabilistic data association (Mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving tracking of three maneuvering targets and a multiscan data window of length two.