A low complexity algorithm based on the weighted <i>l</i><sub>1</sub> inimization for DOA estimation

DUAN Suxin; ZHANG Hao; SUN Xiuzhi; ZHENG Chundi

doi:10.13443/j.cjors.2014030402

Volume 30 Issue 4

Aug. 2015

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CHINESE JOURNAL OF RADIO SCIENCE > 2015 > 30(4): 640-646. > DOI: 10.13443/j.cjors.2014030402

DUAN Suxin, ZHANG Hao, SUN Xiuzhi, ZHENG Chundi. A low complexity algorithm based on the weighted l₁ inimization for DOA estimation[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2015, 30(4): 640-646. doi: 10.13443/j.cjors.2014030402

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A low complexity algorithm based on the weighted l₁ inimization for DOA estimation

1.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2.
China Electronic System Engineering Company, Beijing 100840, China
3.
Navy Marines College, Guangzhou, Guangdong 510430, China

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Received Date: March 03, 2014
Available Online: December 30, 2020
Published Date: August 28, 2015

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Abstract

Abstract

Based on the sparse representation of the array covariance matrix and the Khatri-Rao product of the array response matrix, a low computational complexity sparse recovery method for direction-of-arrival (DOA) estimation is presented. The proposed algorithm not only lessens the number of unknown variable, but also can cut down the dimension of the constraints, which considerably reduce the computational complexity of the second order cone programming. Moreover, a weighted l₁ minimization is designed by using the reciprocal of the Capon spectrum as a weighting vector. As a result, the proposed algorithm can achieve better performance while the computational complexity is reduced. Simulations demonstrate the performance of the proposed method.
- direction-of-arrival (DOA) estimation,
- weighted l₁ minimization,
- sparse recovery,
- uniformly-spaced linear array (ULA)

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A low complexity algorithm based on the weighted l₁ inimization for DOA estimation