A Improved Histogram Probabilistic Multiple Hypothesis Tracking Algorithm for Maneuvering Weak Target
Wei Shangguan, Ying Lu, Jinping Sun · 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · 2019
A improved histogram probabilistic multiple hypothesis tracking (H-PMHT) algorithm based on the extended set-membership filtering (ESMF) is proposed for maneuvering weak targets detection and tracking. As an effective track-before-detect (TBD) method, H-PMHT algorithm is applied to synthesizing target measurements from multi-frame radar observation data directly, so as to avoid the problem of information loss in traditional threshold detection. Since the prior knowledge of maneuvering weak targets in real situations is always unknown but bounded, the ESMF algorithm is applied to obtain the estimated state from the synthesized measurements. Simulation results show that the ESMF based H-PMHT algorithm is capable of detecting and tracking dim targets, and has a better performance in tracking accuracy compared with the Kalman filter based H-PMHT algorithm.