TY - JOUR
T1 - Sparse Bayesian Learning-Based Direct Localization for Distributed Sensor Arrays with Unknown Gain and Phase Errors
AU - Wang, Yuexian
AU - Shi, Qianyuan
AU - Han, Chuang
AU - Wang, Ling
AU - Tellambura, Chintha
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper presents a robust sparse direct position determination (DPD) method for multiple emitters using distributed sensor arrays in the presence of unknown gain-phase errors. The proposed method tackles the problem under a block sparse Bayesian learning (BSBL) framework, which incorporates perturbed steering vector factorization to separate the position parameter from the gain-phase errors, making dictionary completely known without learning. This paper devises a customized hyperparameter update rule for the proposed DPD model within the foundation of the BSBL-EM method, allowing for varying block parameters instead of constraining them to be consistent. The position estimates of emitters are determined by calculating the mean value of the posterior distribution of the reconstructed waveforms. Simulations demonstrate the superior performance of the developed BSBL direct localization method over its state-of-the-art rivals, which exhibits enhanced localization accuracy and robustness against gain-phase errors.
AB - This paper presents a robust sparse direct position determination (DPD) method for multiple emitters using distributed sensor arrays in the presence of unknown gain-phase errors. The proposed method tackles the problem under a block sparse Bayesian learning (BSBL) framework, which incorporates perturbed steering vector factorization to separate the position parameter from the gain-phase errors, making dictionary completely known without learning. This paper devises a customized hyperparameter update rule for the proposed DPD model within the foundation of the BSBL-EM method, allowing for varying block parameters instead of constraining them to be consistent. The position estimates of emitters are determined by calculating the mean value of the posterior distribution of the reconstructed waveforms. Simulations demonstrate the superior performance of the developed BSBL direct localization method over its state-of-the-art rivals, which exhibits enhanced localization accuracy and robustness against gain-phase errors.
KW - block sparsity
KW - direct position determination
KW - gain-phase errors
KW - sparse Bayesian learning
UR - http://www.scopus.com/inward/record.url?scp=105001420848&partnerID=8YFLogxK
U2 - 10.1109/ICASSP48485.2024.10448419
DO - 10.1109/ICASSP48485.2024.10448419
M3 - 会议文章
AN - SCOPUS:105001420848
SN - 1520-6149
SP - 8761
EP - 8765
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
T2 - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Y2 - 14 April 2024 through 19 April 2024
ER -