Wavelet Neural Network Blind Equalization with Cascade Filter Base on RLS in Underwater Acoustic Communication

摘要

For the cost function of CMA blind equalization is not satisfied second normal form and RLS algorithm can not using directly, a cascade filtering method was proposed to solve this problem. The cost function is simplified as second normal form in the method and the Wavelet Neural Network (WNN) was used as blind equalizer, then RLS algorithm can be used to update the network parameters to implement blind equalization. Meanwhile the forgetting factor in RLS algorithm was analyzed and adaptive forgetting factor was proposed to improve the performance. The output error can construct a attenuation function to which nonlinear transform was preformed to adaptive adjust the value of forgetting factor Compared with BP neural network and WNN blind equalization based on gradient descent algorithm and WNN blind equalization based on RLS algorithm with fixed value, the method proposed in this study has faster convergence rate and convergence precision. Acoustic channel simulations and pool experiment proved the method has better performance in underwater communication.