Multiscan Multitarget Tracking Based on a Hybrid Message-Passing Method

The multiple hypothesis tracking (MHT) algorithm is widely applied in various multitarget tracking (MTT) scenarios due to its high association accuracy. The global hypothesis generation (GHG) poses a core and challenging problem within MHT. In this article, we treat the GHG from the message-passing...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2024-06, Vol.24 (11), p.18185-18195
Main Authors: Xu, Hong, Liu, Xinrui, Huang, Libin, Xing, Yizhou, Quan, Yinghui
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computational complexity
Computational efficiency
Data association
Fields (mathematics)
Hypotheses
Logic gates
Markov random field (MRF)
Message passing
message-passing method
multiple hypothesis tracking (MHT)
Multiple target tracking
multitarget tracking (MTT)
Radar tracking
Sensors
Target tracking
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The multiple hypothesis tracking (MHT) algorithm is widely applied in various multitarget tracking (MTT) scenarios due to its high association accuracy. The global hypothesis generation (GHG) poses a core and challenging problem within MHT. In this article, we treat the GHG from the message-passing perspective and propose a hybrid message-passing (HMP) method. First, the GHG problem is mapped onto the Markov random field (MRF), thereby transforming the problem into solving the maximum a posteriori (MAP) estimation on the MRF. Second, the HMP method is introduced within the MRF, especially containing loops. This method utilizes the max-product belief propagation (MP-BP) for rapid pre-selection, obtaining preliminary results. Subsequently, to improve the accuracy of the GHG problem, further refinement is performed on these preliminary results. Based on whether these preliminary results satisfy the independent set condition (ISC), we employ different refinement strategies. For preliminary results that do not meet the ISC, they are fed into the junction tree algorithm (JTA) for refinement. Furthermore, the remaining preliminary results are integrated to obtain the refined result. Finally, we analyze its computational complexity. Through simulations with existing message-passing-based MHT algorithms, we demonstrate the superiority of the HMP method. The proposed HMP method in this article achieves a compromise between accuracy and computational efficiency when addressing MRFs containing loops.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3392485