Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold

被引:0
作者
Xing, Chuanxi [1 ,2 ]
Tan, Guangzhi [1 ,2 ]
Dong, Saimeng [1 ,2 ]
机构
[1] Yunnan Minzu Univ, Coll Elect & Informat Technol, Kunming 650504, Peoples R China
[2] Yunnan Key Lab Unmanned Autonomous Syst, Kunming 650504, Peoples R China
基金
中国国家自然科学基金;
关键词
hydrophone arrays; DOA; empirical mode decomposition; Bayesian learning algorithm; DOA ESTIMATION; SPARSE;
D O I
10.3390/s24175835
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8 degrees between adjacent signal sources. At a low signal-to-noise ratio of -9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.
引用
收藏
页数:26
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