High‐resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning

2015 ◽  
Vol 51 (3) ◽  
pp. 285-287 ◽  
Author(s):  
Wenying Lei ◽  
Baixiao Chen
Keyword(s):  
High Resolution ◽  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Correlated Signals

scholarly journals Acoustic DOA estimation using space alternating sparse Bayesian learning

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zonglong Bai ◽  
Liming Shi ◽  
Jesper Rindom Jensen ◽  
Jinwei Sun ◽  
Mads Græsbøll Christensen
Keyword(s):  
High Resolution ◽  
Bayesian Learning ◽  
Hidden Variables ◽  
Doa Estimation ◽  
Humanoid Robots ◽  
Sparse Bayesian Learning ◽  
Multiple Sources ◽  
Variational Bayesian ◽  
Acoustic Sources ◽  
Array Aperture

AbstractEstimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, including the platform size which puts a constraint on the array aperture. To overcome this problem, a high-resolution DOA estimation algorithm based on sparse Bayesian learning is proposed in this paper. A group sparse prior based hierarchical Bayesian model is introduced to encourage spatial sparsity of acoustic sources. To obtain approximate posteriors of the hidden variables, a variational Bayesian approach is proposed. Moreover, to reduce the computational complexity, the space alternating approach is applied to push the variational Bayesian inference to the scalar level. Furthermore, an acoustic DOA estimator is proposed to jointly utilize the estimated source signals from all frequency bins. Compared to state-of-the-art approaches, the high-resolution performance of the proposed approach is demonstrated in experiments with both synthetic and real data. The experiments show that the proposed approach achieves lower root mean square error (RMSE), false alert (FA), and miss-detection (MD) than other methods. Therefore, the proposed approach can be applied to some applications such as humanoid robots and drones to improve the resolution performance for acoustic DOA estimation especially when the size of the array aperture is constrained by the platform, preventing the use of traditional methods to resolve multiple sources.

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

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

scholarly journals DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

2018 ◽  
Vol 2018 (5) ◽  
pp. 268-273 ◽  
Author(s):  
Fangqing Wen ◽  
Dongmei Huang ◽  
Ke Wang ◽  
Lei Zhang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning
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