@article{CAO2026, 
author = {Jinke CAO and Xiaofei ZHANG and Qihui WU and De BEN},
title = {PHD-based DOA tracking via Taylor compensation and projection cancellation},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {16},
keywords = {DOA tracking, multi-source superimposed measurements, Taylor expansion, projection cancellation, random finite set, pseudo-likelihood function},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2026.33190},
doi = {10.7527/S1000-6893.2026.33190},
abstract = {In multi-domain collaborative sensing and target situation awareness tasks, traditional high-accuracy Direction-of-Arrival (DOA) estimation methods typically rely on a known and fixed number of sources. However, during continuous tracking, the number of targets may vary over time, causing fixed-source-number models to become invalid. Meanwhile, in passive array sensing, multi-target signals often appear in a superimposed form, making it difficult to construct effective single-target likelihood functions and thus limiting the applicability of Random Finite Set (RFS)-based filters in DOA tracking. To address these challenges, this paper proposes a continuous DOA tracking method based on a Taylor-expanded projection-cancellation model. By performing a Taylor expansion of the steering vector around the predicted angle, an extended signal subspace is constructed, and interference from other sources is eliminated using sequential projection operators, resulting in a single-source-equivalent pseudo-spectrum. This enables robust decomposition of multi-source superimposed observations and reliable construction of single-target likelihoods. Furthermore, based on the energy characteristics of the projection-cancellation spectrum and the particle-weight distribution, a birth-death detection mechanism is introduced, which adaptively adjusts the likelihood dimension in the PHD filter without requiring prior source-number estimation. Simulation results demonstrate that the proposed method achieves superior DOA tracking performance compared with conventional approaches, particularly under low SNR conditions and dynamically varying source scenarios.}
}