基于峰度自然对数最大化的信号盲分拣算法和盲波束形成

该文基于峰度自然对数最大化准则,提出了一种自适应一元信号盲分拣算法,提出的算法可以用于一元信号盲分离和进行盲波束形成,与基于峰度值最大化准则的KMA算法相比,收敛速度快,有较强的稳健性,将非线性函数引入学习速率的调节,算法自动选取学习步长,避免了人工选取学习速率不当而导致算法发散。同时,提出了两种复数抽气算法,配合一元信号盲分拣算法可以依次分离多个信号源,仿真试验验证了算法的有效性。用提出的算法在四元线阵上盲分离两个水声信号,结果发现,一元信号盲分离实现的盲波束形成波束图与最优波束接近。

A logarithm-kurtosis based complex algorithm for blind signal extraction and blind beamforming

One source blind extraction can be used to blind beamforming for underwater acoustic arrays. Among existing candidate approaches such as the simple constant modulus Algorithm (CMA), Kurtosis Maximization Algorithm (KMA), etc., KMA can separate both negative and positive kurtosis signals. As KMA is used to separate underwater acoustic signals the convergence rate is low. Present paper applies logarithm of kurtosis to form the objective function, and proposes a one source blind extraction algorithm based on logarithm-kurtosis maximization. At the same time, double deflation algorithms are also proposed to separate more signals in turn. In contrast to KMA convergence rate is improved. A nonlinear function is used in learning so that the algorithm can choose the learning step automatically. Computer simulations verify the proposed algorithm.