A reduced complexity DOA estimation method for real-valued sources in non-uniform sparse linear arrays

2017 ◽  
Author(s):  
Fenggang Sun ◽  
Peng Lan ◽  
Lei Xu ◽  
Bo Sun ◽  
Guowei Zhang
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Linear Arrays ◽  
Reduced Complexity

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 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 An Efficient DOA Estimation Method for Co-Prime Linear Arrays

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 90874-90881 ◽  
Author(s):  
Xiao Yang ◽  
Yide Wang ◽  
Pascal Charge ◽  
Yuehua Ding
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Linear Arrays

Partial spectral search‐based DOA estimation method for co‐prime linear arrays

2015 ◽  
Vol 51 (24) ◽  
pp. 2053-2055 ◽  
Author(s):  
Fenggang Sun ◽  
Peng Lan ◽  
Bin Gao
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Linear Arrays

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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