TY - JOUR
T1 - Sparse spatial spectral estimation
T2 - A covariance fitting algorithm, performance and regularization
AU - Zheng, Jimeng
AU - Kaveh, Mostafa
PY - 2013
Y1 - 2013
N2 - In this paper, the sparse spectrum fitting (SpSF) algorithm for the estimation of directions-of-arrival (DOAs) of multiple sources is introduced, and its asymptotic consistency and effective regularization under both asymptotic and finite sample cases are studied. Specifically, through the analysis of the optimality conditions of the method, we prove the asymptotic, in the number of snapshots, consistency of SpSF estimators of the DOAs and the received powers of uncorrelated sources in a sparse spatial spectra model. Along with this result, an explicit formula of the best regularization parameter of SpSF estimator with infinitely many snapshots is obtained. We then build on these results to investigate the problem of selecting an appropriate regularization parameter for SpSF with finite snapshots. An automatic selector of such regularization parameter is presented based on the formulation of an upper bound on the probability of correct support recovery of SpSF, which can be efficiently evaluated by Monte Carlo simulations. Simulation results illustrating the effectiveness and performance of this selector are provided, and the application of SpSF to direction-finding for correlated sources is discussed.
AB - In this paper, the sparse spectrum fitting (SpSF) algorithm for the estimation of directions-of-arrival (DOAs) of multiple sources is introduced, and its asymptotic consistency and effective regularization under both asymptotic and finite sample cases are studied. Specifically, through the analysis of the optimality conditions of the method, we prove the asymptotic, in the number of snapshots, consistency of SpSF estimators of the DOAs and the received powers of uncorrelated sources in a sparse spatial spectra model. Along with this result, an explicit formula of the best regularization parameter of SpSF estimator with infinitely many snapshots is obtained. We then build on these results to investigate the problem of selecting an appropriate regularization parameter for SpSF with finite snapshots. An automatic selector of such regularization parameter is presented based on the formulation of an upper bound on the probability of correct support recovery of SpSF, which can be efficiently evaluated by Monte Carlo simulations. Simulation results illustrating the effectiveness and performance of this selector are provided, and the application of SpSF to direction-finding for correlated sources is discussed.
KW - Consistency
KW - direction-of-arrival (DOAs) estimation
KW - regularization parameter selection
KW - sparse spectrum fitting (SpSF)
UR - http://www.scopus.com/inward/record.url?scp=84877904749&partnerID=8YFLogxK
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U2 - 10.1109/TSP.2013.2256903
DO - 10.1109/TSP.2013.2256903
M3 - Article
AN - SCOPUS:84877904749
SN - 1053-587X
VL - 61
SP - 2767
EP - 2777
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 11
M1 - 6494328
ER -