Improved spatial smoothing techniques for DOA estimation of coherent signals

1991 ◽  
Vol 39 (5) ◽  
pp. 1208-1210 ◽  
Author(s):  
W. Du ◽  
R.L. Kirlin
Keyword(s):  
Doa Estimation ◽  
Spatial Smoothing ◽  
Coherent Signals ◽  
Smoothing Techniques

scholarly journals The Analysis of Using Spatial Smoothing for DOA Estimation of Coherent Signals in Sparse Arrays

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haiyun Xu ◽  
Weijia Cui ◽  
Yuxi Du ◽  
Fengtong Mei ◽  
Bin Ba
Keyword(s):  
Numerical Experiments ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
The Other ◽  
Feasibility Analysis ◽  
Spatial Smoothing ◽  
Coherent Signals ◽  
Sparse Arrays

When there is coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms for direction-of-arrival (DOA) estimation using difference coarray fail. In order to solve the problems, this paper analyzes the feasibility of using spatial smoothing in sparse arrays. Firstly, we summarize the two types of sparse arrays, one consisting of identical sparse subarrays and the other consisting of several uniform linear subarrays. Then, we give the feasibility analysis and the processes of applying spatial smoothing. At last, we discuss the performance of the number of detectable coherent signals in different sparse arrays. Numerical experiments prove the conclusions proposed by the paper.

scholarly journals Coherent Target Direction-of-Arrival Estimation for Coprime Arrays: From Spatial Smoothing Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dongming Wu ◽  
Fangzheng Liu ◽  
Zhihui Li ◽  
Zhenzhong Han
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Spatial Smoothing ◽  
Phase Ambiguity ◽  
Coherent Signals ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Common Peak ◽  
Coprime Arrays

In this paper, we investigate the issue of direction-of-arrival (DOA) estimation of multiple signals in coprime arrays. An algorithm based on multiple signal classification (MUSIC) and forward and backward spatial smoothing (FBSS) is used for DOA estimation of this signal caused by multipath and interference. The large distance between adjacent elements of each subarray in the coprime arrays will bring phase ambiguity issues. According to the feature of the coprime number, the ambiguity problem can be eliminated. The correct DOA estimation can be obtained by searching for the common peak of the spatial spectrum and finding the overlapping peaks in the MUSIC spectrum of the two subarrays. For the rank deficit problem caused by the coherent signal, the FBSS algorithm is used for signal preprocessing before the MUSIC algorithm. Theoretical analysis and simulation results show that the algorithm can effectively solve the rank deficiency and phase ambiguity problems caused by coherent signals and sparse arrays in the coprime arrays.

DOA Estimation of Coherent Signals Using one Snapshot

2010 ◽  
Vol 32 (3) ◽  
pp. 604-608 ◽  
Author(s):  
Xin Xie ◽  
Guo-lin Li ◽  
Hua-wen Liu
Keyword(s):  
Doa Estimation ◽  
Coherent Signals

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 Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

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.

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