Grid Adaptive DOA Estimation Method in Monostatic MIMO Radar Using Sparse Bayesian Learning

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.

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

IEEE Access ◽

10.1109/access.2019.2930531 ◽

2019 ◽

Vol 7 ◽

pp. 99907-99916 ◽

Cited By ~ 15

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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DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

The Journal of Engineering ◽

10.1049/joe.2017.0872 ◽

2018 ◽

Vol 2018 (5) ◽

pp. 268-273 ◽

Cited By ~ 2

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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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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