scholarly journals Fast DOA Estimation Algorithms for Sparse Uniform Linear Array With Multiple Integer Frequencies

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29952-29965 ◽  
Author(s):  
Aihua Liu ◽  
Xin Zhang ◽  
Qiang Yang ◽  
Weibo Deng
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Uniform Linear Array ◽  
Estimation Algorithms

DOA Estimation of Noncircular Signals for Coprime Linear Array via Polynomial Root-Finding Technique

2020 ◽  
pp. 2150028
Author(s):  
Hui Zhai ◽  
Zheng Li ◽  
Xiaofei Zhang
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Uniform Linear Array ◽  
Estimation Algorithms ◽  
Root Finding ◽  
Noncircular Signals ◽  
Peak Search ◽  
Simulation Results ◽  
Polynomial Root Finding

In this paper, we investigate the direction of arrival (DOA) estimation problem of noncircular signals for coprime linear array (CLA). From the perspective of the CLA as extracted from a filled uniform linear array (ULA), a noncircular root-MUSIC algorithm is proposed to estimate the DOA which can avoid the spectral peak search and lower the computational complexity. Due to the noncircular characteristic, the proposed algorithm enables to resolve more sources than sensors. Meanwhile, the proposed algorithm has better angle estimation performance than some conventional DOA estimation algorithms. Numerical simulation results illustrate the performance of the proposed method.

DOA Estimation for Uniform Linear Array with Compressive Sensing

2015 ◽  
Vol 713-715 ◽  
pp. 1239-1243
Author(s):  
Ying Zhang ◽  
Guang Yao Xin ◽  
Xiao Fei Zhang
Keyword(s):  
Compressive Sensing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Uniform Linear Array ◽  
High Demand ◽  
Estimation Algorithms

This paper discusses that the application of compressive sensing in direction of arrival (DOA) estimation. Traditional DOA estimation algorithms, such as MUSIC, ESPRIT, have shortcomings of high demand of initialization and sufficient number of snapshots and high sensitivity to signal-to-noise ratio (SNR). The proposed DOA estimation algorithm via OMP method based on compressed sensing (CS) can solve the above-mentioned problem and has good estimation performance. Computer simulations verify the effectiveness of the OMP algorithm.

Comparison between DOA Estimation Subspace methods for incoherent and coherent signals

2015 ◽  
Vol 1 ◽  
pp. 18
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Tang Bin ◽  
Jumma Mary Atieno ◽  
Abdelazeim Abdalla
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Far Field ◽  
Matlab Simulation ◽  
Subspace Methods ◽  
Coherent Signals ◽  
Uniform Linear Array ◽  
Pros And Cons ◽  
Basic Concepts ◽  
And Performance

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

scholarly journals Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 424 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Linxi Liu ◽  
Man Feng
Keyword(s):  
Compressed Sensing ◽  
Mutual Coupling ◽  
Linear Array ◽  
State Of The Art ◽  
Coupling Effect ◽  
Estimation Method ◽  
Doa Estimation ◽  
System Model ◽  
Reconstruction Problem ◽  
Uniform Linear Array

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

DOA Estimation for Antenna Array with Partially-Well Sensors via Low-Rank Matrix Completion

2013 ◽  
Vol 756-759 ◽  
pp. 3977-3981 ◽  
Author(s):  
Hua Xing Yu ◽  
Xiao Fei Zhang ◽  
Jian Feng Li ◽  
De Ben
Keyword(s):  
Missing Values ◽  
Linear Array ◽  
Matrix Completion ◽  
Doa Estimation ◽  
Low Rank ◽  
Uniform Linear Array ◽  
Completion Problem ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix

In this paper, we address the angle estimation problem in linear array with some ill sensors (partially-well sensors), which only work well randomly. The output of the array will miss some values, and this can be regarded as a low-rank matrix completion problem due to the property that the number of sources is smaller than the number of the total sensors. The output of the array, which is corrupted by the missing values and the noise, can be complete via the Optspace method, and then the angles can be estimated according to the complete output. The proposed algorithm works well for the array with some ill sensors; moreover, it is suitable for non-uniform linear array. Simulation results illustrate performance of the algorithm.

A Sparse-Based Approach for DOA Estimation and Array Calibration in Uniform Linear Array

2016 ◽  
Vol 16 (15) ◽  
pp. 6018-6027 ◽  
Author(s):  
Hongqing Liu ◽  
Luming Zhao ◽  
Yong Li ◽  
Xiaorong Jing ◽  
Trieu-Kien Truong
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Uniform Linear Array ◽  
Array Calibration
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