TY - GEN
T1 - Sparse Representation-Based DOA Estimation with Concentration Ratio Criteria
AU - Liu, Aifei
AU - Xu, Fujia
AU - Du, Boyang
AU - Wang, Yanting
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A sparse representation-based direction-of-arrival (DOA) estimation method is proposed which defines a concentration ratio (CR) criterion for selecting the regularization parameter, shorten as the SRCR method. The proposed SRCR method performs regardless of the statistics of noise and thus it is applicable in the case of noise with unknown statistics. In particular, the SRCR method defines the CR of the recovered sparse vector as a criterion for selecting the regularization parameter. In addition, it optimizes the regularization parameter to ensure the CR is near to 1. By this way, the optimized regularization parameter recovers the sparsest signal vector, which results in correct DOA estimation. Simulation results demonstrate that the SRCR method is independent of the statistics of noise, and it performs significantly better than the SR-based DOA estimation method with the discrepancy principle (DP) for the regularization parameter selection.
AB - A sparse representation-based direction-of-arrival (DOA) estimation method is proposed which defines a concentration ratio (CR) criterion for selecting the regularization parameter, shorten as the SRCR method. The proposed SRCR method performs regardless of the statistics of noise and thus it is applicable in the case of noise with unknown statistics. In particular, the SRCR method defines the CR of the recovered sparse vector as a criterion for selecting the regularization parameter. In addition, it optimizes the regularization parameter to ensure the CR is near to 1. By this way, the optimized regularization parameter recovers the sparsest signal vector, which results in correct DOA estimation. Simulation results demonstrate that the SRCR method is independent of the statistics of noise, and it performs significantly better than the SR-based DOA estimation method with the discrepancy principle (DP) for the regularization parameter selection.
KW - DOA estimation
KW - regularization parameter
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85149938087&partnerID=8YFLogxK
U2 - 10.1109/ICICSP55539.2022.10050690
DO - 10.1109/ICICSP55539.2022.10050690
M3 - Conference Proceeding
AN - SCOPUS:85149938087
T3 - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
SP - 27
EP - 30
BT - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
Y2 - 26 November 2022 through 28 November 2022
ER -