Affiliation:
1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2. Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314033, China
Abstract
This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is modeled by a random matrix. The EOs are classified according to the size information of the ellipse. Usually, detection, tracking, and classification are processed step-by-step. However, step-by-step processing ignores the coupling relationship between detection, tracking, and classification, resulting in information loss. In fact, detection, tracking, and classification affect each other, and JDTC is expected to be beneficial for achieving better overall performance. In the multi-target tracking problem based on RFS, the overall performance of the PMBM filter satisfying the conjugate priors has been verified to be superior to other filters. Specifically, the PMBM filter propagates multiple MB simultaneously during iterative updates and model the distribution of hitherto undetected EOs. At present, the PMBM filter is only applied to multiple extended objects tracking problem. Therefore, we consider using the PMBM filter to solve the JDTC problem of multiple EOs and further improve JDTC performance. Furthermore, the closed-form implementation based on the product of a gamma Gaussian inverse Wishart (GGIW) and class probability mass function (PMF) is proposed. The details of parameters calculation in the implementation process and the derivation of class PMF are presented in this paper. Simulation experiments verify that the proposed algorithm, named the JDTC-PMBM-GGIW filter, performs well in comparison to the existing JDTC strategies for multiple extended objects.
Funder
National Natural Science Foundation of China
Science and Technology on Electronic Information Control Laboratory
Subject
General Earth and Planetary Sciences
Reference54 articles.
1. Paul, M.M., Sergiu, N., and Radu, D. (2021). Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images. Sensors, 21.
2. Mahler, R. (2007). Statistical Multisource-Multitarget Information Fusion, Artech House.
3. Tracking low elevation targets in the presence of multipath propagation;Kumar;IEEE Trans. Aerosp. Electron. Syst.,1994
4. Multiple Target Tracking With Unresolved Measurements;Angle;IEEE Signal Process. Lett.,2021
5. Poisson models for extended target and group tracking;Gilholm;Signal Data Process. Small Targets,2005
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