WSN-PHD: A Novel Moving Target DOA Tracking Algorithm
This letter investigates the multi-target direction of arrival (DOA) tracking problem under wireless sensor networks (WSNs), where each node is equipped with a uniform linear array (ULA). A WSN probability hypothesis density (WSN-PHD) tracking algorithm is proposed with two key innovations: 1) an exponential accumulation (EA) fusion strategy that aggregates multi-node measurements to resolve occlusions in WSNs, thereby improving tracking continuity under time-varying targets, and 2) a non-circular phase-assisted likelihood function to enhance target tracking accuracy. Numerical results show that the proposed algorithm achieves average optimal sub-pattern assignment (OSPA) distance and location errors reduction of 78.18% and 76.82% compared to the algorithm without EA fusion.
Authors
- Meng Sun (ORCID: https://orcid.org/0000-0002-4565-5399)
- Xudong Dong (ORCID: https://orcid.org/0009-0001-3640-3018)
- Jun Zhao (ORCID: https://orcid.org/0000-0002-7914-196X)
- Yide Wang (ORCID: https://orcid.org/0000-0002-1461-2003)
- Xiaofei Zhang (ORCID: https://orcid.org/0000-0003-1464-1987)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Institut d'Électronique et des Technologies du numéRique (FR)
- Hangzhou Dianzi University (CN)
- China Jiliang University (CN)
- Nantes Université (FR)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/jiot.2025.3594698
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00