On Bayesian MHT for well-separated targets in densely cluttered environment

Abstract

Certain radar applications must cope with rather extreme operational conditions (densely cluttered environment/relatively small detection probabilities). Tracking in such situations calls for refined data association and processing techniques. By simulation results we demonstrate the applicability of Bayesian multiple hypothesis tracking (MRT) to well-separated targets detected with a mechanically rotating radar. Special emphasis is placed on air situations characterized by massively occurring data association conflicts.