Compressed sensing based low complexity 2D-DOA estimation by separation and pair-matching approach

In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

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Pair-Matching Method by Signal Covariance Matrices for 2D-DOA Estimation

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2014.2331076 ◽

2014 ◽

Vol 13 ◽

pp. 1199-1202 ◽

Cited By ~ 17

Author(s):

Yinsheng Wei ◽

Xiaojiang Guo

Keyword(s):

Doa Estimation ◽

Covariance Matrices ◽

Matching Method ◽

Pair Matching ◽

2D Doa Estimation

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A Low-Complexity 2D-DOA Estimation Algorithm with Massive URA

Journal of Physics Conference Series ◽

10.1088/1742-6596/1575/1/012186 ◽

2020 ◽

Vol 1575 ◽

pp. 012186

Author(s):

Zhou Lu ◽

Baobao Li ◽

Xin Lai ◽

Haowei Zeng

Keyword(s):

Estimation Algorithm ◽

Doa Estimation ◽

Low Complexity ◽

2D Doa Estimation

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Enhanced 2D-DOA Estimation for Large Spacing Three-Parallel Uniform Linear Arrays

International Journal of Antennas and Propagation ◽

10.1155/2018/3679494 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7

Author(s):

Dong Zhang ◽

Yongshun Zhang ◽

Cunqian Feng

Keyword(s):

Elevation Angle ◽

Estimation Algorithm ◽

Doa Estimation ◽

Low Complexity ◽

Estimation Accuracy ◽

Linear Arrays ◽

Eigen Value ◽

Directional Cosine ◽

2D Doa Estimation ◽

Value Decomposition

An enhanced two-dimensional direction of arrival (2D-DOA) estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs) is proposed in this paper. Firstly, we use the propagator method (PM) to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD) or singular value decompostion (SVD) to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.

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Two-Dimensional DOA Estimation in Compressed Sensing with Compressive-Reduced Dimension-lp-MUSIC

International Journal of Antennas and Propagation ◽

10.1155/2015/792181 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Weijian Si ◽

Xinggen Qu ◽

Lutao Liu ◽

Zhiyu Qu

Keyword(s):

Compressed Sensing ◽

Estimation Method ◽

Doa Estimation ◽

Superior Performance ◽

Two Dimensional ◽

Improve Performance ◽

Performance Simulation ◽

Pair Matching ◽

Reduced Dimension ◽

Simulation Results

This paper presents a novel two-dimensional (2D) direction of arrival (DOA) estimation method in compressed sensing (CS) to remove the estimation failure problem and achieve superior performance. The proposed method separates the steering vector into two parts to construct two corresponding noise subspaces by introducing electric angles. Then, electric angles are estimated based on the constructed noise subspaces. In order to estimate the azimuth and elevation angles in terms of estimates of electric angles, arc-tangent operations are exploited. The arc-tangent is a one-to-one function and allows the value of the argument to be larger than unity so that the proposed method never fails. The proposed method can avoid pair matching to reduce the computational complexity and extend the number of snapshots to improve performance. Simulation results show that the proposed method can avoid estimation failure occurrence and has superior performance as compared to existing methods.

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