一种宽带水声目标DOA估计方法
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摘要
针对现有的基于压缩感知的宽带水声目标DOA估计方法中存在估计计算时间长、失败率高的问题,将稀疏贝叶斯学习重构方法应用到宽带水声目标DOA估计中,采用定点方法对SBL算法中的核心超参量进行求解,提出一种基于稀疏贝叶斯学习的宽带水声目标DOA估计方法.通过仿真实验,验证了该方法的可行性与有效性,并证实该算法相对于常用的波束形成算法、MUSIC算法、SBL算法以及BP算法具有运算速度快、重构精度高等优点.
The existing direction of arrival(DOA) estimation methods for wideband underwater acoustic target based on CS have longer computation time and higher estimation failure rates. To solve these problems, a new direction of arrival estimation algorithm of wideband underwater acoustic target based on sparse Bayesian learning is proposed, which uses sparse Bayesian learning as the reconstructing algorithm to DOA estimation for wideband underwater acoustic target and employs fixed-point method to obtain the value of core hyperparameter to estimate the DOA. Simulation results verify the feasibility and effectiveness of the proposed algorithm and confirm this algorithm had advantages of high speed for the process of computation and superior recovery performance to common beamforming algorithm, MUSIC algorithm, and normative SBL method with BP algorithm.
The existing direction of arrival(DOA) estimation methods for wideband underwater acoustic target based on CS have longer computation time and higher estimation failure rates. To solve these problems, a new direction of arrival estimation algorithm of wideband underwater acoustic target based on sparse Bayesian learning is proposed, which uses sparse Bayesian learning as the reconstructing algorithm to DOA estimation for wideband underwater acoustic target and employs fixed-point method to obtain the value of core hyperparameter to estimate the DOA. Simulation results verify the feasibility and effectiveness of the proposed algorithm and confirm this algorithm had advantages of high speed for the process of computation and superior recovery performance to common beamforming algorithm, MUSIC algorithm, and normative SBL method with BP algorithm.
引文
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[ 2 ] 潘磊, 束鑫, 祁云嵩. 一种改进的快速压缩跟踪算法[J]. 江苏科技大学学报(自然科学版), 2015,29(2):175-179.DOI: 10.3969/j.issn.1673-4807.2015.02.014.PAN Lei, SHU Xin, QI Yunsong. An improved fast compressive tracking algorithm[J].Journal of Jiangsu University of Science and Technology(Natural Science Edition),2015,29(2):175-179.DOI: 10.3969/j.issn.1673-4807.2015.02.014.(in Chinese)
[ 3 ] MALIOUTOV D, CETIN M, WILLSKY A. A sparse signal reconstruction perspective for source localization with sensor arrays[J].IEEE Transactions on Signal Processing, 2005, 53(8):3010-3022. DOI:10.1109/TSP.2005.850882
[ 4 ] CAI T, XU G, ZHANG J. On recovery of sparse signals via l1minimization[J]. IEEE Trans. Inf. Theory, 2010, 55(7):3388-3397. DOI:10.1109/TIT.2009.2021377.
[ 5 ] 孙磊, 王华力, 许广杰, 等. 基于稀疏贝叶斯学习的高效DOA估计方法[J]. 电子与信息学报,2013,35(5): 1196-1201.DOI: 10.3724/SP.J.1146.2012.01429.SUN Lei, WANG Huali, XU Guangjie, et al. Efficient Direction-of-arrival estimation via sparse Bayesian Learning[J]. Journal of Electronics and Information Technology,2013,35(5): 1196-1201.DOI: 10.3724/SP.J.1146.2012.01429.(in Chinese)
[ 6 ] ZHANG Z, RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-926. DOI: 10.1109/JSTSP.2011.2159773.
[ 7 ] ZHANG Z, RAO B D. Recovery of block sparse signals using the framework of block sparse Bayesian learning[C]//Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on IEEE, 2012:3345-3348.DOI: 10.1109/ICASSP.2012.6288632.
[ 8 ] MACKAY D. Bayesian Interpolation[J]. Neural Computation, 1992,4(3): 415-447.
[ 9 ] TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001,1(1):211-244. DOI: 10.1162/15324430152748236.
[10] BALKAN O, KREUTZ-DELGADO K, MAKEIG S. Localization of more sources than sensors via jointly-sparse Bayesian Learning[J]. IEEE Signal Processing Letters,2014, 21(2):131-134. DOI: 10.1109/lsp.2013.2294862.
[11] ZHANG Z. Sparse signal recovery exploiting spatiotemporal correlation[J]. Dissertations & Theses-Gradworks, 2012.
[12] 刘寅, 吴顺君, 吴明宇, 等. 基于空域稀疏性的宽带DOA估计方法[J]. 航空学报,2012;33(11):2028-2038.DOI: 1000-6893(2012)11-2028-11.LIU Yan, WU Shunjun, WU Mingyu, et al.Wideband DOA estimation based on spatial sparseness[J]. Acta Aeronautica Astronautica Sinica, 2012,33(11):2028-2038.DOI:1000-6893(2012)11-2028-11.(in Chinese)
[13] 杨海蓉, 张成, 丁大为, 等. 压缩传感理论与重构算法[J]. 电子学报,2011, 39(1):142-148. DOI: 0372-2112(2011)01-0142-07.
[14] 朱智慧. 基于稀疏重建的高精度水声目标DOA估计方法研究[D].镇江:江苏科技大学,2015.
[15] WIPF D P, RAO B D. An empirical bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007,55(7):3704-3716. DOI: 10.1109/TSP.2007.894265.