TY - GEN
T1 - Data-adaptive regularization for DOA estimation using sparse spectrum fitting
AU - Zheng, J.
AU - Kaveh, M.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - Regularization parameter selection is critical to the performance of many sparsity-exploiting Direction-Of-Arrival (DOA) estimation algorithms. In this paper, we propose an automatic selector for choosing this parameter in the DOA estimation algorithm presented in [1], which is based on the analysis of its optimality conditions. This selector requires very limited prior information and is computationally efficient. Through simulation examples, the effectiveness and robustness of the selector are illustrated.
AB - Regularization parameter selection is critical to the performance of many sparsity-exploiting Direction-Of-Arrival (DOA) estimation algorithms. In this paper, we propose an automatic selector for choosing this parameter in the DOA estimation algorithm presented in [1], which is based on the analysis of its optimality conditions. This selector requires very limited prior information and is computationally efficient. Through simulation examples, the effectiveness and robustness of the selector are illustrated.
KW - Direction-Of-Arrival
KW - Regularization Parameter Selection
KW - Sparse Representation
UR - http://www.scopus.com/inward/record.url?scp=84890526302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890526302&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638401
DO - 10.1109/ICASSP.2013.6638401
M3 - Conference contribution
AN - SCOPUS:84890526302
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3957
EP - 3961
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -