To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this pap...To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.展开更多
The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing s...The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.展开更多
基金supported by the National Natural Science Foundation of China(61761048)the Basic Research Special General project of Yunnan Province(202101AT070132)the Yunnan Minzu University Graduate Research Innovation Fund Project(2024SKY122).
文摘To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.
基金supported by the National Natural Science Foundation of China(11574120,U1636117)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing,Ministry of Education,China(UASP1503)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20161359)Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project
文摘The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.
