scholarly journals An Efficient Improved OGWSBI Algorithm for Accurate Off-Grid DOA Estimation of Coherent Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangjun Xu ◽  
Mingwei Shen ◽  
Di Wu ◽  
Daiyin Zhu
Keyword(s):  
Bayesian Inference ◽  
Unitary Transformation ◽  
Order Approximation ◽  
Doa Estimation ◽  
Higher Order ◽  
Estimation Accuracy ◽  
Real Field ◽  
Coherent Signals ◽  
Steering Vector ◽  
Grid Model

The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off-grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher-order off-grid model and unitary transformation for off-grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher-order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.

2D DOA Estimation Using Sparse Representation of Higher-Order Power of Covariance Matrix

2014 ◽  
Vol 998-999 ◽  
pp. 779-783
Author(s):  
Zheng Luo ◽  
Fei Yu ◽  
Lin Wu ◽  
Yuan Liu
Keyword(s):  
Covariance Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Higher Order ◽  
Eigenvalue Decomposition ◽  
Coherent Signals ◽  
Sparse Decomposition ◽  
Sparse Signal Representation ◽  
2D Doa Estimation ◽  
Source Signals

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a sparse signal representation of higher-order power of covariance matrix is proposed. Through applying the higher-order power of covariance matrix to construct a new sparse decomposition vector, this algorithm avoids the estimation of incident signal number and eigenvalue decomposition. And the hierarchical granularity-dictionary is studied, which forms the over-complete dictionary adaptively in the light of source signals’ distribution. Compared with MUSIC and L1-SVD, this algorithm not only provides a better 2D DOA performance but also possesses the capability of coherent signals estimation. Theoretical analysis and simulation results demonstrate the validity and robust of the proposed algorithm.

Off-Grid DOA Estimation of Coherent Signals Using Weighted Sparse Bayesian Inference

2021 ◽  
Author(s):  
Xiangjun Xu ◽  
Mingwei Shen ◽  
Shengwei Zhang ◽  
Di Wu ◽  
Daiyin Zhu
Keyword(s):  
Bayesian Inference ◽  
Doa Estimation ◽  
Coherent Signals

scholarly journals AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zhi-Chao Sha ◽  
Zhang-Meng Liu ◽  
Zhi-Tao Huang ◽  
Yi-Yu Zhou
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Coherent Signals ◽  
Model Based ◽  
Mixing Matrix ◽  
Simulation Results

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

scholarly journals A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Chen ◽  
Hao Wu ◽  
Yongxiang Liu
Keyword(s):  
Good Accuracy ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Low Snr ◽  
Sample Covariance ◽  
Lower Complexity ◽  
The Difference

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

scholarly journals DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Panhe Hu ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Bistatic Radar ◽  
Spatial Difference ◽  
Coherent Signals ◽  
Vhf Radar ◽  
Multipath Environment ◽  
Passive Bistatic Radar

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

scholarly journals Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4320 ◽  
Author(s):  
Ming-Yang Cao ◽  
Xingpeng Mao ◽  
Xiaozhuan Long ◽  
Lei Huang
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Parameters Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Rectangular Array ◽  
Vector Sensor ◽  
Electromagnetic Vector Sensor ◽  
Vector Sensors ◽  
Uniform Rectangular Array

This paper addresses the direction-of-arrival (DOA) estimation problem using a uniform rectangular array with electromagnetic vector-sensors in correlated/coherent signal environments. The polarization information is separated from the steering matrix to decorrelate the signals. By developing a tensorial structured received measurements of the array, we propose a tensor-based eigenvector DOA estimation method. Then we apply the forward-backward averaging to the tensor since it obeys the centro-Hermitian structure. In addition, a tensor-based polarization parameters estimation method is presented. The proposed algorithms are superior to the state-of-the-art algorithms in terms of estimation accuracy of coherent signals while only demand a modest computation burden comparing with the latter ones. Simulation results are given to demonstrate the effectiveness of the proposed methods under different scenarios.

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.

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