2017
DOI: 10.3390/s17051068
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Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar

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 l0-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 f… Show more

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Cited by 16 publications
(12 citation statements)
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“…There are many kinds of system clutter, and the common probability density distributions include Weibull distribution, Rayleigh distribution, log-normal distribution, and K distribution. The expression of the Rayleigh distribution and the K distribution are shown in (18) and 4, respectively. The expressions for the Weibull distribution and the log-normal distribution are:…”
Section: Signal Receiving and Processing Modulementioning
confidence: 99%
See 1 more Smart Citation
“…There are many kinds of system clutter, and the common probability density distributions include Weibull distribution, Rayleigh distribution, log-normal distribution, and K distribution. The expression of the Rayleigh distribution and the K distribution are shown in (18) and 4, respectively. The expressions for the Weibull distribution and the log-normal distribution are:…”
Section: Signal Receiving and Processing Modulementioning
confidence: 99%
“…A pilot pattern algorithm based on the least squares (LS) algorithm for channel estimation in MIMO-orthogonal frequency division multiplexing (OFDM) systems is discussed in [17]. The joint smoothed l 0 -norm algorithm for fast sparse DOA estimation has been proposed for multiple measurement vectors in MIMO radar systems [18]. The results indicated that it can eliminate the white or colored Gaussian noises and achieve better DOA estimation performance.…”
Section: Introductionmentioning
confidence: 99%
“…In [38], a reweighted ℓ 0 norm minimization method with fast iterations is proposed for the DOA estimation in MIMO radar. A fast sparse DOA estimation algorithm for both the white and colored Gaussian noises is proposed in [39] in the scenario with multiple measurement vectors. Xie et al [40] developed a covariancevector sparsity-aware estimator to estimate the DOA from the MIMO radar.…”
Section: Introductionmentioning
confidence: 99%
“…They achieve better performance than the method in [10] with the same number of nonzero taps. Furthermore, for the pursuit of both sparse promotion and improved accuracy, the sparse signals reconstruction algorithms inspired by l 1 and weighted l 1 regularization schemes are proposed in [17,18]; the joint smoothed l 0 norm algorithm for direction-of-arrival estimation in MIMO radar is proposed in [19]. …”
Section: Introductionmentioning
confidence: 99%