Work

WSN-PHD: A Novel Moving Target DOA Tracking Algorithm

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2025.3594698

Authors: Jun Zhao, Xudong Dong, Meng Sun, Xiaofei Zhang, Yide Wang

Institutions: China Jiliang University, Hangzhou Dianzi University, Nanjing University of Aeronautics and Astronautics, Centre National de la Recherche Scientifique, Institut d'Électronique et des Technologies du numéRique, Nantes Université

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Hangzhou Dianzi University

OpenAlex ID: W4412836917

FWCI: 1.663

Cites:

Cited by: 4

Related to: 10