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Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction
Received date: 2013-04-27
Revised date: 2013-11-05
Online published: 2013-11-29
Supported by
National Natural Science Foundation of China (61302141); Director Foundation of CEMEE (CEMEE2014Z0202B)
The performance of the subspace-based direction of arrival (DOA) estimation methods can be improved significantly via effective exploitation of the non-circularity of the incident signals, but the shortcomings of these methods in adaptation to demanding scenarios, such as low signal-to-noise ratio (SNR) and limited snapshots, can hardly be made up. The sparse Bayesian learning (SBL) technique is introduced in this paper to deal with the DOA estimation problem of non-circular signals. The spatial sparsity of the incident signals is exploited together with their non-circularity property, and the covariance and conjugate covariance matrices of the array outputs of non-circular signals are decomposed jointly under a sparsity constraint to reconstruct the spatial spectrum of the incident signals, and the DOA estimates are finally obtained according to the spectrum peak locations. This method is robust against inter-signal correlation, and its superiorities in adaptation to demanding scenarios as well as in DOA estimation precision are demonstrated by the simulation results.
LIU Zhangmeng , ZHOU Yiyu , WU Haibin . Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014 , 35(3) : 821 -827 . DOI: 10.7527/S1000-6893.2013.0461
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