A Robust Algorithm for Source Number Detection and 2-D DOA Estimation Based on Real-valued Computation

2006 ◽  
Author(s):  
Liu Fulai ◽  
Zhou Xiyuan ◽  
Li Chun ◽  
Wang Jinkuan
Keyword(s):  
Doa Estimation ◽  
Robust Algorithm ◽  
Source Number

scholarly journals DOA Estimation without Source Number for Cyber-Physical Interactions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Jianzhong Li ◽  
Xiaobo Gu ◽  
Ruidian Zhan ◽  
Xiaoming Xiong ◽  
Yuan Liu
Keyword(s):  
A Priori ◽  
Doa Estimation ◽  
A Priori Knowledge ◽  
Second Order Statistics ◽  
Oblique Projector ◽  
Physical Interactions ◽  
Simulation Results ◽  
The Impact ◽  
The Relationship ◽  
Source Number

In this paper, a direction of arrival (DOA) estimator is proposed to improve the cyber-physical interactions, which is based on the second-order statistics without a priori knowledge of the source number. The impact of noise will firstly be eliminated. Then the relationship between the processed covariance matrix and the steering matrix is studied. By applying the elementary column transformation, an oblique projector will be designed without the source number. At last, a rooting method will be adopted to estimate the DOAs with the constructed projector. Simulation results show that the proposed method performs as well as other methods, which requires that the source number must be known.

Joint DOA estimation and source number detection for arrays with arbitrary geometry

2017 ◽  
Vol 140 ◽  
pp. 149-160 ◽  
Author(s):  
Falamarz Izedi ◽  
Mahmood Karimi ◽  
Mostafa Derakhtian
Keyword(s):  
Doa Estimation ◽  
Arbitrary Geometry ◽  
Source Number

scholarly journals A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2296
Author(s):  
Yuanyuan Yao ◽  
Hong Lei ◽  
Wenjing He
Keyword(s):  
Doa Estimation ◽  
Spatial Filter ◽  
Spatial Spectrum ◽  
Data Driven ◽  
Spatial Filters ◽  
Variable Source ◽  
Matrix Reconstruction ◽  
Colored Gaussian Noise ◽  
Source Number ◽  
New Framework

Estimating directions of arrival (DOA) without knowledge of the source number is regarded as a challenging task, particularly when coherence among sources exists. Researchers have trained deep learning (DL)-based models to attack the problem of DOA estimation. However, existing DL-based methods for coherent sources do not adapt to variable source numbers or require signal independence. Herein, we put forward a new framework combining parallel DOA estimators with Toeplitz matrix reconstruction to address the problem. Each estimator is constructed by connecting a multi-label classifier to a spatial filter, which is based on convolutional-recurrent neural networks. Spatial filters divide the angle domain into several sectors, so that the following classifiers can extract the arrival directions. Assisted with Toeplitz-based method for source-number determination, pseudo or missed angles classified by the estimators will be reduced. Then, the spatial spectrum can be more accurately recovered. In addition, the proposed method is data-driven, so it is naturally immune to signal coherence. Simulation results demonstrate the predominance of the proposed method and show that the trained model is robust to imperfect circumstances such as limited snapshots, colored Gaussian noise, and array imperfections.

Single-snapshot DOA estimation and source number detection

1997 ◽  
Vol 4 (4) ◽  
pp. 109-111 ◽  
Author(s):  
B.M. Radich ◽  
K.M. Buckley
Keyword(s):  
Doa Estimation ◽  
Source Number ◽  
Single Snapshot

scholarly journals An Efficient DOA Estimation Algorithm Based on Diagonal-Symmetric Loading

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yangyang Xie ◽  
Biao Wang ◽  
Feng Chen
Keyword(s):  
Super Resolution ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Minimum Variance ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Minimum Variance Distortionless Response ◽  
Symmetric Loading ◽  
Subspace Processing ◽  
Source Number

In order to solve the problem that the subspace-like direction of arrival (DOA) estimation performs poor due to the error of sources number, this paper proposes a new super-resolution DOA estimation algorithm based on the diagonal-symmetric loading (DSL). Specifically, orthogonality principle of the minimum eigenvector of the specific covariance matrix and the source number estimation based on the improved K-means method were adopted to construct the spatial spectrum. Then, by considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters was given and verified by numerical experiment. To evaluate the effectiveness of the proposed algorithm, this paper compared it with the methods of minimum variance distortionless response (MVDR) and new signal subspace processing (NSSP). Experimental results prove that the proposed DSL has higher resolution and better estimation accuracy than the MVDR and NSSP.

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