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Abstract
Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the -norm minimization based methods, such as -SVD (singular value decomposition), RV (real-valued) -SVD and RV -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.
Highlights
Colocated multiple-input multiple-output (MIMO) radar has attracted a growing interest recently because it can achieve higher resolution and better parameter identification compared with conventional phased-array radar [1]
The simulation results of the proposed method are compared with those of the l1 -SVD [7], RV l1 -SVD [16], RV l1 -SRACV [14] and reweighted smoothed l0 -norm (RSL0) [22]
By means of the minimum description length (MDL) principle or the Akaike information criterion (AIC) principle [31], the prior knowledge of the target number is assumed to be known as P
Summary
Colocated multiple-input multiple-output (MIMO) radar has attracted a growing interest recently because it can achieve higher resolution and better parameter identification compared with conventional phased-array radar [1]. In the area of sensor array signal processing, sparse representation is an important technique to estimate the parameters. When reconstructing the sparse signal, to avoid the NP-hard (non-deterministic polynomial-time hard) l0 -norm minimization, different methods such as those based on the relaxed constraint l1 -norm minimization [7], the focal underdetermined system solution (FOCUSS) [8] and the sparse Bayesian learning (SBL) [9] have been proposed. These algorithms were originally designed for the single measurement vector (SMV) problem.
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