Abstract
The performance of multi-channel Compressive Sensing (CS)-based Direction-of-Arrival (DOA) estimation algorithm degrades when the gains between Radio Frequency (RF) channels are inconsistent, and when target angle information mismatches with system sensing model. To solve these problems, a novel single-channel CS-based DOA estimation algorithm via sensing model optimization is proposed. Firstly, a DOA sparse sensing model using single-channel array considering the sensing model mismatch is established. Secondly, a new single-channel CS-based DOA estimation algorithm is presented. The basic idea behind the proposed algorithm is to iteratively solve two CS optimizations with respect to target angle information vector and sensing model quantization error vector, respectively. In addition, it avoids the loss of DOA estimation performance caused by the inconsistent gain between RF channels. Finally, simulation results are presented to verify the efficacy of the proposed algorithm.
Highlights
Compressive Sensing (CS) theory, deduced from signal processing and information theories [1] [2] [3], has been widely applied in radar, image processing, wireless communication and many other engineering fields [4] [5] [6] [7] [8]
These two algorithms treat the over-complete based matrices as the redundant dictionaries, obtained from the angle interval of uniform quantization interested area, which cannot ensure that the corresponding sensing matrix meets the Restricted Isometry Property (RIP) condition [13]. [14] uses random Gauss matrix to perform compressive sampling in space-domain and adopts Regularized Multi-vectors Focal Undetermined System Solver (RMFOCUSS) algorithm to achieve high-resolution DOA estimation
By observing (8), we can conclude that sampling of space-domain signals through single channel array can be regarded as performing random projection of measurement matrix Ψ on receive signal x (t ), converting the multiple measurement vectors (MMV) problem to a single measurement vector (SMV) problem [23]
Summary
Compressive Sensing (CS) theory, deduced from signal processing and information theories [1] [2] [3], has been widely applied in radar, image processing, wireless communication and many other engineering fields [4] [5] [6] [7] [8]. [12] adopts an array element randomly distributed antenna to perform compressive sampling in space domain to reduce the number of RF channels of the system. These two algorithms treat the over-complete based matrices as the redundant dictionaries, obtained from the angle interval of uniform quantization interested area, which cannot ensure that the corresponding sensing matrix meets the Restricted Isometry Property (RIP) condition [13]. [14] uses random Gauss matrix to perform compressive sampling in space-domain and adopts Regularized Multi-vectors Focal Undetermined System Solver (RMFOCUSS) algorithm to achieve high-resolution DOA estimation.
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