scholarly journals Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang-Meng Liu ◽  
Zheng Liu ◽  
Dao-Wang Feng ◽  
Zhi-Tao Huang
Keyword(s):  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Relevance Vector Machine ◽  
Sparse Bayesian Learning ◽  
Spatial Filters ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatial Sparsity ◽  
Array Geometry

A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.

scholarly journals Grid Reconfiguration Method for Off-Grid DOA Estimation

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1209 ◽  
Author(s):  
Yun Ling ◽  
Huotao Gao ◽  
Guobao Ru ◽  
Haitao Chen ◽  
Boya Li ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Dynamic Parameters ◽  
Fission Process ◽  
Simulation Results ◽  
Grid Points ◽  
Grid Algorithms

Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.

Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

2016 ◽  
Vol 129 ◽  
pp. 183-189 ◽  
Author(s):  
Yi Wang ◽  
Minglei Yang ◽  
Baixiao Chen ◽  
Zhe Xiang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning

USTC-MUSIC Algorithm for DOA Estimation of Coherent Sources Using Single Vector Hydrophone

2014 ◽  
Vol 530-531 ◽  
pp. 530-533
Author(s):  
Jin Fang Cheng ◽  
Chao Ran Zhang ◽  
Wei Zhang
Keyword(s):  
Correlation Matrix ◽  
Temporal Correlation ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Vector Hydrophone ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatio Temporal

The MUSIC algorithm cannot deal with the problem of DOA estimation of coherent sources, this paper proposes the USTC (unitary spatio-temporal correlation matrices)-MUSIC algorithm using single vector hydrophone to solve this problem, by utilizing the unitary spatio-temporal correlation matrix instead of the covariance matrix. The simulation results demonstrate that the USTC-MUSIC algorithm has a better ability to distinguish the coherent sources from different directions than the spatial smoothing MUSIC algorithm.

scholarly journals Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 302 ◽  
Author(s):  
Yun Ling ◽  
Huotao Gao ◽  
Sang Zhou ◽  
Lijuan Yang ◽  
Fangyu Ren
Keyword(s):  
Autonomous Vehicles ◽  
Bayesian Learning ◽  
Rapid Development ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Vehicle Localization ◽  
Localization System ◽  
Sparse Signal Representation

With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

scholarly journals Off-Grid DOA Estimation for Colocated MIMO Radar via Reduced-Complexity Sparse Bayesian Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99907-99916 ◽  
Author(s):  
Tingting Liu ◽  
Fangqing Wen ◽  
Lei Zhang ◽  
Ke Wang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Reduced Complexity
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