DOA Estimation Based on the Multipath of the MIMO Radar Target

Ji, Ai Guo; Wang, Tao; Zhu, Zhigang

doi:10.4028/www.scientific.net/amm.229-231.2029

Cited by 1 publication

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References 7 publications

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“…The performance of the ML and MUSIC methods is adversely affected by low SNR, however [12]. Therefore, Ji et al [15] proposed the spatial MUSIC algorithm, while Zhang et al [16] used the colony algorithm to solve Processes 2020, 8, 1429 2 of 12 computationally complex problems; however, these methods cannot deal with the DOA inaccuracy caused by excessive noise. Madurasinghe [10] suggested the power domain ML method to formulate a new objective function to solve the problem of low-energy non-uniform noise through estimation of the actual noise; however, the objective function did not propose a solution for the general low SNR and high non-uniform noise method.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Hung

2020

Processes

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In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

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Section: Introductionmentioning

confidence: 99%

DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Hung

2020

Processes

View full text Add to dashboard Cite

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DOA Estimation Based on the Multipath of the MIMO Radar Target

Cited by 1 publication

References 7 publications

DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

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