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针对以具有时序结构的稀疏贝叶斯学习(Temporally multiple sparse Bayesian learning,TMSBL)为重构算法的水声目标DOA(Direction-of-arrival)估计方法存在运算速度慢的问题,结合块稀疏贝叶斯学习(Block-spare Bayesian learning,BSBL)理论框架下DOA估计模型与特点,采用MacKay提出的定点方法(Fixed-point method)对TMSBL算法中的核心超参量进行求解,提出一种快速的水声目标方位估计稀疏贝叶斯学习的方法,该方法具有运算速度快,重构概率高的特点,并通过实验仿真从运算时间、失败率和均方根误差等方面与TMSBL算法进行比较,验证了该方法的可行性与有效性。
Aiming at the problem of slow computation speed of DOA (Direction-of-arrival) estimation method based on TMSBL with time-series structure reconstruction algorithm, In the framework of Block-Sparse Bayesian Learning (BSBL) theory, the DOA estimation model and its characteristics are analyzed. The key parameters of the TMSBL algorithm are solved by using the fixed-point method proposed by MacKay. A fast underwater acoustic Target orientation estimation sparse Bayesian learning method, the method has the characteristics of fast computation speed and high reconstruction probability, and compared with the TMSBL algorithm from the aspects of computation time, failure rate and root mean square error The feasibility and effectiveness of this method.