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 A Novel Algorithm to Estimate Closely Spaced Source DOA

2017 ◽  
Vol 7 (4) ◽  
pp. 2109 ◽  
Author(s):  
Sidi Mohamed Hadj Irid ◽  
Samir Kameche ◽  
Said Assous
Keyword(s):  
Classical Music ◽  
Linear Array ◽  
Doa Estimation ◽  
Spatial Sampling ◽  
Signal Classification ◽  
Far Field ◽  
Uniform Linear Array ◽  
Resolution Method ◽  
Multiple Signal Classification ◽  
High Resolution Method

<p>In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.</p>

Fuzzy System for DOA Estimation of Coherent Signals

2013 ◽  
Vol 427-429 ◽  
pp. 575-581
Author(s):  
Ya Ling Chen ◽  
Chien Chou Lin
Keyword(s):  
Fuzzy System ◽  
High Performance ◽  
Expert Knowledge ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Fuzzy Logic Systems ◽  
Significant Performance ◽  
And Performance ◽  
Low Sensitivity ◽  
Resolution Estimation

This paper presents an efficient direction-of-arrival (DOA) Estimator for dealing with coherent signals. The empirical results show that significant performance degradation occurs when coherent signals coexist. Therefore, an utilizes the low sensitivity of Bartlett algorithm in estimation of DOAs for coherent signals to yield a low-resolution estimation of DOAs as initial search angle and uses fuzzy logic systems with incorporating expert knowledge to improve the resolution and performance of estimation of DOAs in coherent signals environment. Finally, numerical example was analyzed to illustrate high performance of the proposed method and to confirm the designed procedure.

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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