一种基于互相关神经网络的声呐盲波束形成方法研究

A sonar blind beamforming method based on a cross correlation neural network

针对水声环境和水声信号的特点,提出了一种基于神经网络的声呐盲波束形成算法。该方法利用水声信号的循环平稳特性把波束形成权向量的求解问题转化为阵列接收信号互相关函数的奇异值分解问题;引入一种互相关神经网络求解阵列接收信号相关函数的奇异值,从而减小了运算的代价,可高效实现盲波束形成。提出的改进互耦Hebbian学习规则有效地提高了神经网络权值的更新速度,为问题的实时求解提供了有效的途径。该方法还能抑制噪声和干扰的影响,表现出较强的顽健性。仿真实验验证了算法的正确性。

A blind beamforming algorithm based on a neural network is presented according to the characteristic of underwater acoustic environment and signal. This method transforms the question of estimating beamforming weight vectors into the one of computing the SVD of the cross correlation matrix of array input signals and their frequency shift signals. A cross correlation neural network is introduced to compute the SVD of the cross correlation matrix so as to reduce the computational complexity and carry out the blind beamforming more efficiently. The improved cross-coupled Hebbian learning rule presented in this paper can accelerate convergence rate of the weight vectors. Therefore, it is more promising in the practical use. This method can restrain noise and interference. Simulation proves its correctness.