A Novel 2-D Coherent DOA Estimation Method Based on Dimension Reduction Sparse Reconstruction for Orthogonal Arrays

• Published in: Sensors (Basel, Switzerland) Switzerland), 2016-09, Vol.16 (9), p.1496
• Main Authors: Wang, Xiuhong, Mao, Xingpeng, Wang, Yiming, Zhang, Naitong, Li, Bo
• Format: Article
• Language: English
• Subjects: coherent sources; dimension reduction sparse reconstruction; direction of arrival (DOA); redundant sub-dictionary; two-dimensional (2-D) DOA estimation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16091496

Based on sparse representations, the problem of two-dimensional (2-D) direction of arrival (DOA) estimation is addressed in this paper. A novel sparse 2-D DOA estimation method, called Dimension Reduction Sparse Reconstruction (DRSR), is proposed with pairing by Spatial Spectrum Reconstruction of Sub-Dictionary (SSRSD). By utilizing the angle decoupling method, which transforms a 2-D estimation into two independent one-dimensional (1-D) estimations, the high computational complexity induced by a large 2-D redundant dictionary is greatly reduced. Furthermore, a new angle matching scheme, SSRSD, which is less sensitive to the sparse reconstruction error with higher pair-matching probability, is introduced. The proposed method can be applied to any type of orthogonal array without requirement of a large number of snapshots and a priori knowledge of the number of signals. The theoretical analyses and simulation results show that the DRSR-SSRSD method performs well for coherent signals, which performance approaches Cramer-Rao bound (CRB), even under a single snapshot and low signal-to-noise ratio (SNR) condition.