Multiple hypotheses tracking for maneuvering targets in clutter environment

Abstract

In this paper, an optimal filter for maneuvering target tracking in clutter environment is derived by combining measurement association hypotheses and target model transition hypotheses. The optimal filter is not realizable since it should consider exponentially increasing hypotheses. To reduce the hypotheses, a new hypotheses pruning technique is proposed. And then a realizable suboptimal filter is suggested. Simulation results show that the proposed filter produces smaller estimation errors and takes better track maintenance than the interacting multiple model probabilistic data association filter.