Grid Reconfiguration Method for Off-Grid DOA Estimation

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.

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Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

10.21203/rs.3.rs-26103/v1 ◽

2020 ◽

Author(s):

Caiyi Tang ◽

Qianli Wang ◽

Zhiqin Zhao ◽

Zaiping Nie ◽

Chuanfeng Niu

Keyword(s):

Processing Time ◽

Bayesian Learning ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Sparse Bayesian Learning ◽

Transformation Technique ◽

Computational Burden ◽

First Time ◽

Single Snapshot

Abstract Sparse Bayesian learning (SBL) has been successfully applied in solving the problem of direction-of-arrival (DOA) estimation. However, SBL needs multiple snapshots to ensure accuracy and costs huge computational workload. To reduce the requirement of snapshot and computational burden, a DOA estimation method based on the randomize-then-optimize (RTO) algorithm is first time introduced. RTO algorithm uses the optimization and Metropolis-Hastings approach to avoid the “learning” process of SBL in updating hyperparameters. And in order to apply RTO algorithm in the circumstance of signal with Laplace prior, a prior transformation technique is first induced. Compared with conventional CS based DOA methods, the proposed method has a better accuracy with single snapshot and shorter processing time. Some simulations are conducted to demonstrate the effectiveness of the proposed method.

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Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

Journal of Marine Science and Engineering ◽

10.3390/jmse9020127 ◽

2021 ◽

Vol 9 (2) ◽

pp. 127

Author(s):

Guolong Liang ◽

Zhibo Shi ◽

Longhao Qiu ◽

Sibo Sun ◽

Tian Lan

Keyword(s):

Ambient Noise ◽

Bayesian Learning ◽

Colored Noise ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Noise Model ◽

Superior Performance ◽

Sparse Bayesian Learning ◽

Vector Hydrophone

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

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

Sensors ◽

10.3390/s20010302 ◽

2020 ◽

Vol 20 (1) ◽

pp. 302 ◽

Cited By ~ 1

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.

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A Robust Sparse Bayesian Learning-Based DOA Estimation Method With Phase Calibration

IEEE Access ◽

10.1109/access.2020.3013610 ◽

2020 ◽

Vol 8 ◽

pp. 141511-141522

Author(s):

Zhimin Chen ◽

Wanxing Ma ◽

Peng Chen ◽

Zhenxin Cao

Keyword(s):

Bayesian Learning ◽

Estimation Method ◽

Doa Estimation ◽

Sparse Bayesian Learning ◽

Phase Calibration

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An Improved Direction-of-Arrival Estimation Method Based on Sparse Reconstruction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.135 ◽

2012 ◽

Vol 263-266 ◽

pp. 135-138

Author(s):

Xue Bing Han ◽

Zhao Jun Jiang

Keyword(s):

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Sparse Reconstruction ◽

Direction Of Arrival Estimation ◽

Sensing Matrix ◽

Signal Model ◽

Multiple Measurement Vectors ◽

Underdetermined System ◽

Simulation Results

In this paper, we account for efficient approach of direction-of-arrival estimation based on sparse reconstruction of sensor measurements with an overcomplete basis. MSD-FOCUSS ( MMV Synchronous Descending FOCal Underdetermined System Solver) algorithm is developed against to sparse reconstruction in multiple-measurement-vectors (MMV) system where noise perturbations exist in both the measurements and sensing matrix. The paper shows how sparse-signal model of DOA estimation is established and MSD-FOCUSS is derived, then the simulation results illustrate the advantage of MSD-FOCUSS when it is used to solve the problem of DOA estimation.

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