scholarly journals A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1367 ◽  
Author(s):  
Fenggang Sun ◽  
Bin Gao ◽  
Lizhen Chen ◽  
Peng Lan
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Linear Arrays

scholarly journals A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 218 ◽  
Author(s):  
Wei He ◽  
Xiao Yang ◽  
Yide Wang
Keyword(s):  
Computational Complexity ◽  
Degrees Of Freedom ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Covariance Matrices ◽  
Linear Arrays ◽  
Estimation Performance ◽  
Uniform Property ◽  
Finding Method

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals DOA Tracking of Two-Dimensional Coherent Distribution Source Based on Fast Approximated Power Iteration

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Tao Wu ◽  
Pengtao Zhang ◽  
Yiwen Li ◽  
Yangjun Gao ◽  
Chaoqi Fu ◽  
...  
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Distribution Functions ◽  
Tracking Algorithm ◽  
Signal Subspace ◽  
Two Dimensional ◽  
Model Errors ◽  
Rectangular Array ◽  
Linear Arrays ◽  
Power Iteration

Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.

scholarly journals Low-complexity DOA estimation method for a co-prime linear array

2019 ◽  
Vol 2019 (20) ◽  
pp. 6503-6506
Author(s):  
Xuchen Wu ◽  
Xiaopeng Yang ◽  
Bowen Han ◽  
Feng Xu
Keyword(s):  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity

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

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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