A new Cardinalized Probability Hypothesis Density Filter with Efficient Track Continuity and Extraction
Zhejun Lu, Weidong Hu, Yongxiang Liu, Thia Kirubaraian · 2018
The cardinalized probability hypothesis density (CPHD) filter was proposed as a practical approximation to the multi-target Bayes filter with tractable computational complexity. However, the CPHD filter has limitations in dealing with missed detections, extracting target state in its particle implementations, and in maintaining track continuity. In this paper, a new improved CPHD filter is proposed as a solution to address these limitations, with efficient track continuity and extraction. This filter inherits tractable computational complexity and addresses the drawbacks of the standard CPHD filter. The proposed filter is implemented using Gaussian mixtures, and simulation results demonstrate the effectiveness of the proposed filter compared to the conventional multi-taraet filter in challenging scenarios.