An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking

• Published in: IEEE transactions on aerospace and electronic systems Vol. 58; no. 6; pp. 5256 - 5275
• Main Authors: Kropfreiter, Thomas, Meyer, Florian, Hlawatsch, Franz
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Complexity; Computational modeling; Filtering; Filtering algorithms; multiobject tracking; Multiple target tracking; multitarget tracking; object detection; point processes; Probability density function; Radio frequency; random finite sets (RFSs); Sea measurements; sequential estimation; Trajectory
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3168252

In this article, we propose an efficient random finite set (RFS)-based algorithm for multiobject tracking, in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.