Evaluation of tracking filter performance for maneuvering targets in dense environment

Abstract

A new method for maneuvering target tracking in a dense environment is presented. This is an extension and improvement of the conventional joint probabilistic data association. The maneuver acceleration is assumed to be limited to a time invariant set of discrete values and switched values according to the Markov process. In this method, the maneuver of a target increases the prediction covariance as compared with that obtained by standard Kalman filter equations, and so, the validation gate size varies automatically with the maneuver of the target. The performance of this method is evaluated in terms of tracking success rates by computer simulation.