2021/10/25 by Shuoyuan Xu, Hyo-Sang Shin, Hyo‐Sang Shin +4
Computer Science · Engineering · Mathematics · #Advanced Chemical Sensor Technologies #Algorithm #Artificial intelligence #Bayesian probability #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Inference #Information Theory (cs.IT) #Mathematics #Probabilistic logic #Robotics (cs.RO) #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #Tracking (education) #cs.IT #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.2110.11954
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/10/25 · openalex publication_date 2021/10/25 · arxiv updated 2021/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper proposes a novel multi-target tracking (MTT) algorithm for scenarios with arbitrary numbers of measurements per target. We propose the variational probabilistic multi-hypothesis tracking (VPMHT) algorithm based on the variational Bayesian expectation-maximisation (VBEM) algorithm to resolve the MTT problem in the classic PMHT algorithm. With the introduction of variational inference, the proposed VPMHT handles track-loss much better than the conventional probabilistic multi-hypothesis tracking (PMHT) while preserving a similar or even better tracking accuracy. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.