【摘要】 为解决小波加权多模盲均衡算法在实现水声信道均衡的过程中,由于采用随机梯度下降法最小化非凸性代价函数,而导致的收敛速度慢和稳态误差大等问题,本文提出了基于退火狼群算法优化的小波加权多模盲均衡算法。该算法将模拟退火算法嵌入狼群围攻行为结束后和狼群发生更新之前,增强基本狼群算法的全局寻优能力,利用其最小化小波加权多模盲均衡算法中的非凸性代价函数。仿真结果表明,本文算法与小波加权多模盲均衡算法以及基本狼群算法优化的小波加权多模盲均衡算法相比,具有收敛迅速、稳态误差小,星座图更清晰等优点。更多还原

【Abstract】 An orthogonal wavelet transform weighted multi-modulus blind equalization algorithm has been used to realize the underwater acoustic channel equalization. However, in the process of achieving underwater acoustic channel equalization, the problems of featuring lower convergence speed and bigger residual mean square error are caused by the application of minimizing non-convex cost function of stochastic gradient descent method. In order to resolve those problems, an orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on simulated annealing optimization wolf pack algorithm is proposed in this paper. The proposed algorithm simulated annealing algorithm would be embedded after the wolfs’ siege and before the wolves are updated, which could enhance the global optimization capability of the basic wolf algorithm and help to minimize the non-convex cost function of the orthogonal wavelet transform weighted multi-modulus blind equalization algorithm. As the simulation results have shown, when the algorithm proposed in this paper is compared with the orthogonal wavelet transform weighted multi-modulus blind equalization algorithm and the orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on wolf pack algorithm, good advantages of higher convergence, smaller residual mean square error and clearer constellations diagram are obtained with the proposed algorithm.更多还原