Improved variational sparse Bayesian learning off-grid DOA estimation method

WANG Xuhu1, JIN Xu1, HOU Yujun1, XU Zhenhua2, TIAN Yu1, ZHANG Qunfei3

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (13) : 134-143.

PDF(3402 KB)
PDF(3402 KB)
Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (13) : 134-143.

Improved variational sparse Bayesian learning off-grid DOA estimation method

  • WANG Xuhu1, JIN Xu1, HOU Yujun1, XU Zhenhua2, TIAN Yu1, ZHANG Qunfei3
Author information +
History +

Abstract

In order to improve the processing speed and direction-of-arrival (DOA) estimation performance of array signal, an improved variational sparse Bayesian learning method for off-grid DOA estimation is proposed. The real-value transformation is utilized in this method, by which the signal of the vectorized covariance matrix in the complex domain is transformed into the real domain. Combining the ideas of variational sparse Bayesian learning and grid evolution, the grid can adaptively evolve from the initial uniform grid to the non-uniform grid during the iteration process, whereby the evolved grid points gradually approximate the real source orientation through alternate iterations of grid update and grid fission. Compared with the traditional compression sensing methods, the simulation results of the proposed method not only decrease operation and improve the efficiencies of the operations but also have higher estimation accuracy and resolution of DOA. Especially in the case of fewer snapshots and the low signal-to-noise ratio (SNR), these advantages become more evident. The effectiveness and engineering practicality of the proposed method can be further verified by the data processing results of the on-lake experiments.

Key words

direction of arrival estimation / off-grid model / real-valued transformation / grid evolution / variational sparse Bayesian learning

Cite this article

Download Citations
WANG Xuhu1, JIN Xu1, HOU Yujun1, XU Zhenhua2, TIAN Yu1, ZHANG Qunfei3. Improved variational sparse Bayesian learning off-grid DOA estimation method[J]. Journal of Vibration and Shock, 2024, 43(13): 134-143

References

[1] Yan F G, Shuai L, Wang J, et al. Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation[J]. Signal Processing, 2018, 152: 1-12. [2] Wu C, Ye C B. DOA Estimation for unfolded coprime arrays: successive-MUSIC algorithm[J]. IOP Conference Series: Materials Science and Engineering, 2020, 719: 012035-012041. [3] 李海森,李 珊,周 天. 基于空间平滑的多波束测深声呐相干分布源方位估计[J]. 振动与冲击,2014, 33(04): 138-142. Li Hai-sen, Li Shan, Zhou Tian. DOA estimation based on spatial smoothing for multi-beam bathymetric sonar coherent distributed sources [J]. Journal of Vibration and Shock, 2014, 33(04): 138-142. [4] Li H B, Zhang Q F, Feng W K. Matrix completion ESPRIT for DOA estimation using nonuniform linear array[J]. IEICE Transactions on Communications, 2019, 102 (12): 2253-2259. [5] Zhang W, Han Y, Jin M, et al. An improved ESPRIT-like algorithm for coherent signals DOA estimation[J]. IEEE Communications Letters, 2020, 24(2): 339-343. [6] Wang R, Wang Y, Li Y P, et al. Geometric algebra-based ESPRIT algorithm for DOA estimation[J]. Sensors, 2021, 21(17): 5933-5947. [7] Huang Q H, Zhang G F, Fang Y. DOA estimation using block variational sparse Bayesian learning[J]. Chinese Journal of Electronics, 2017, 26(4): 768-772. [8] Jiang H, Tang W G, Pang S X. Off-grid DOA estimation for nested array using atomic norm minimisation[J]. Electronics Letters, 2018, 54(23): 1344-1346. [9] Liu B Y, Matsushita S Y, Xu L. DOA Estimation with small snapshots using weighted mixed norm based on spatial filter[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 16183-16187. [10] Liu Y, Dong N, Zhang X H, et al. DOA estimation for massive MIMO systems with unknown mutual coupling based on block sparse Bayesian learning[J]. Sensors, 2022, 22(22): 8634-8651. [11] Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing: A publication of the IEEE Signal Processing Society, 2005, 53(8): 3010-3022. [12] Babacan S, Molina R, Katsaggelos A K. Bayesian compressive sensing using laplace priors [J]. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 2010, 19(1): 53-63. [13] Yang Z, Xie L H, Zhang C S. Off-grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Trans. Signal Processing, 2013, 61(1): 38-43. [14] Dai J S, Bao X, Xu W C, et al. Root sparse Bayesian learning for off-grid DOA estimation[J]. IEEE Signal Processing Letters, 2017, 24(1): 46-50. [15] Huang H P, So H C, Zoubir A M. Off-grid direction-of-arrival estimation using second-order Taylor approximation[J]. Signal Processing, 2022, 196: 108513-108519. [16] LIU D H, ZHAO Y B. Real-valued sparse Bayesian learning algorithm for off-grid DOA estimation in the beamspace[J]. Digital Signal Processing, 2022, 121: 103322-103328. [17] Zeng H W, Yue H, Cao J K, Zhang X F. Real-valued direct position determination of quasi-stationary signals for nested arrays: Khatri-Rao subspace and unitary transformation[J]. Sensors, 2022, 22(11): 4209-4224. [18] Zhang Y H, Yang Y X, Yang L. Off-grid DOA estimation through variational Bayesian inference in colored noise environment[J]. Digital Signal Processing, 2021, 111: 102967-102981. [19] Wang P Y, Yang H C, Ye Z F. An off-grid wideband DOA estimation method with the variational Bayes expectation-maximization framework[J]. Signal Processing, 2022, 193: 108423-108430. [20] Wang H F, Wang X P, Huang M X, et al. A novel variational SBL approach for off-grid DOA detection under nonuniform noise[J]. Digital Signal Processing, 2022, 128: 103622-103630. [21] 高 阳,陈俊丽,杨广立. 基于酉变换和稀疏贝叶斯学习的离格DOA估计[J]. 通信学报,2017, 38(06): 177-182. Gao Yang, Chen Jun-li, Yang Guang-li. Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning[J]. Journal on Communications, 2017, 38(06): 177-182. [22] Wang Q L, Zhao Z Q, Chen Z M, et al. Grid evolution method for DOA estimation[J]. IEEE Transactions on Signal Processing, 2018, 66(9): 2374-2383.
PDF(3402 KB)

316

Accesses

0

Citation

Detail

Sections
Recommended

/