TY - GEN
T1 - A Robust Direct Position Determination Method Based on Block Sparse Bayesian Learning in the Presence of Unknown Mutual Coupling
AU - Shi, Qianyuan
AU - Wang, Yuexian
AU - Han, Chuang
AU - Li, Rongfeng
AU - He, Chengyan
AU - Wang, Ling
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a block sparse Bayesian learning (BSBL) based robust direct position determination (DPD) method is proposed for locating multiple emitters in the presence of unknown mutual coupling in distributed sensor arrays. This method initiates by conducting a matrix transformation on the actual steering vector, effectively segregating the position parameter from the mutual coupling coefficient. Besides, the BSBL algorithm is designed to iteratively update the hyperparameters. Ultimately, the position of the radio emitter is estimated based on the mean value of the posterior distribution derived from the reconstruction process. At the same time, a mutual coupling coefficient estimator is provided based on the principle of subspace, which can achieve more accurate localization by compensating the mutual coupling coefficient in the sparse dictionary. Simulation results verify that the developed BSBL scheme outperforms the state-of-the-art solution in terms of localization accuracy and robustness to unknown mutual coupling.
AB - In this paper, a block sparse Bayesian learning (BSBL) based robust direct position determination (DPD) method is proposed for locating multiple emitters in the presence of unknown mutual coupling in distributed sensor arrays. This method initiates by conducting a matrix transformation on the actual steering vector, effectively segregating the position parameter from the mutual coupling coefficient. Besides, the BSBL algorithm is designed to iteratively update the hyperparameters. Ultimately, the position of the radio emitter is estimated based on the mean value of the posterior distribution derived from the reconstruction process. At the same time, a mutual coupling coefficient estimator is provided based on the principle of subspace, which can achieve more accurate localization by compensating the mutual coupling coefficient in the sparse dictionary. Simulation results verify that the developed BSBL scheme outperforms the state-of-the-art solution in terms of localization accuracy and robustness to unknown mutual coupling.
KW - block sparse Bayesian learning
KW - direct position determination
KW - mutual coupling
KW - radio emitters
UR - http://www.scopus.com/inward/record.url?scp=85184849640&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC59353.2023.10400318
DO - 10.1109/ICSPCC59353.2023.10400318
M3 - 会议稿件
AN - SCOPUS:85184849640
T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Y2 - 14 November 2023 through 17 November 2023
ER -