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

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.

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

IEEE Access ◽  
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

Underdetermined DOA Estimation Method for Wideband Signals Using Joint Nonnegative Sparse Bayesian Learning

2017 ◽  
Vol 24 (5) ◽  
pp. 535-539 ◽  
Author(s):  
Nan Hu ◽  
Bing Sun ◽  
Yi Zhang ◽  
Jisheng Dai ◽  
Jiajun Wang ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Wideband Signals

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.

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

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.

scholarly journals A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 347
Author(s):  
Song Liu ◽  
Lan Tang ◽  
Yechao Bai ◽  
Xinggan Zhang
Keyword(s):  
Kalman Filter ◽  
Fast Moving ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Estimation Problem ◽  
Sparse Bayesian Learning ◽  
Moving Targets

The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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