Abstract
A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative optimization alternatively on target angle information vector and sensing matrix mismatching error vector is proposed. The simulation result indicates that the proposed algorithm possesses higher angle resolution and estimation accuracy compared with conventional CS-based DOA estimation algorithms.
Highlights
The strong scatter centers of targets in area of interest only occupy finite angle resolution cells, and the echo signal of targets is sparse, so compressive sensing (CS) theory is widely studied in direction-of-arrival (DOA) estimation applications [1,2,3,4,5]
In [2], an array with element randomly distributed is adopted to perform compressive sampling on space-domain signal, reducing the number of receiving frontend channels of the array. Both [1, 2] treat the overcomplete based matrixes as the redundant dictionaries, obtained from the angle interval of uniform quantization area of interest, which cannot ensure that the corresponding sensing matrix meets the restricted isometry property (RIP) [3]
A novel compressive sensing- (CS-)based DOA estimation algorithm suitable for the situation when sensing matrix mismatches target angle information is proposed in this paper
Summary
The strong scatter centers of targets in area of interest only occupy finite angle resolution cells, and the echo signal of targets is sparse, so compressive sensing (CS) theory is widely studied in direction-of-arrival (DOA) estimation applications [1,2,3,4,5]. In [2], an array with element randomly distributed is adopted to perform compressive sampling on space-domain signal, reducing the number of receiving frontend channels of the array. Both [1, 2] treat the overcomplete based matrixes as the redundant dictionaries, obtained from the angle interval of uniform quantization area of interest, which cannot ensure that the corresponding sensing matrix meets the restricted isometry property (RIP) [3]. Reference [5] uses random Gauss matrix to perform compressive sampling on space-domain signal and adopts regularized multivectors focal undetermined system solver (RMFOCUSS) algorithm to achieve high-resolution estimation. International Journal of Antennas and Propagation alternatively on target angle information vector and sensing matrix mismatching error vector is proposed to achieve highresolution DOA estimates
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