Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals

Qisen WANG; Hua YU; Jie LI; Chao DONG; Fei JI; Yankun CHEN

doi:10.11999/JEIT200656

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 708-716

Qisen WANG, Hua YU, Jie LI, Chao DONG, Fei JI, Yankun CHEN. Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals[J]. Journal of Electronics & Information Technology, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656

Citation:

Qisen WANG, Hua YU, Jie LI, Chao DONG, Fei JI, Yankun CHEN. Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals[J]. Journal of Electronics & Information Technology, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656

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Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals

doi: 10.11999/JEIT200656

Qisen WANG^{1, 2},
Hua YU^{3, 2, 1},
Jie LI^{3
,
,},
Chao DONG^{2, 4},
Fei JI^{3, 1},
Yankun CHEN^{2, 4}

1.
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
2.
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
3.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
4.
South China Sea Marine Survey and Technology Center, Ministry of Natural Resources, Guangzhou 510300, China

Funds: The National Natural Science Foundation of China (U1809211, 61771202, 61971198), The Key Program of Marine Economy Development Special Foundation of Guangdong Province (GDNRC [2020]009), Guangdong Basic and Applied Basic Research Foundation (2019A151501104), Open Funding Project of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources (MESTA-2020-A005)

Received Date: 2020-08-03
Rev Recd Date: 2021-01-25

Available Online: 2021-02-04

Publish Date: 2021-03-22

Abstract

Abstract

Off-grid Direction Of Arrival (DOA) estimation aims to handle the mismatch between the actual DOA and the presumed grid points. For DOAs of closely spaced signals, sparse grid points leads to degradation of accuracy and resolution, although dense grid points can improve the estimation accuracy, it significantly increases the computational burden. To solve this problem, this paper proposes a Sparse Bayesian Learning (SBL) based algorithm for DOA estimation of closely spaced signals, which consists of three steps. Firstly, a novel fixed point iterative method for signal of Laplace priori is derived to pre-estimate the hyper-parameters by maximizing the array’s marginal likelihood function, which results in faster convergence speed compared to other classical SBL algorithms. Secondly, a new grid interpolation method is implemented to optimize a set of grid points, and signal power and noise variance are estimated again to resolve closely spaced DOAs. Finally, an expression of maximum likelihood function with respect to angle is derived to improve the search of the off-grid DOA. Simulation results show that the proposed algorithm has higher accuracy and resolution for closely spaced DOAs with higher computational efficiency compared with other classical algorithms based on SBL.
- Direction Of Arrival estimation,
- Off-grid,
- Sparse Bayesian Learning (SBL),
- Closely spaced

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References(18)

References

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