时变混合系统的自适应动量项快速盲源分离算法

Adaptive Momentum Fast Blind Source Separation Algorithm for Time-varying Mixing System

现有的多数盲源分离算法都是假设混合系统是时不变的,然而在实际的通信系统中混合系统常常是时变的。提出一种渐变的时变混合系统模型,并针对该渐变模型和现有的突变模型提出了收敛速度较快的盲源分离算法,该算法使用均方误差指数加权和的形式定义代价函数,并且在算法学习过程中引入了自适应动量项。仿真结果表明,所提算法在时变环境中较现有算法有更快的收敛速度,能有效地跟踪时变混合系统,并能抗多音干扰。

Most of existing blind source separation algorithms are developed by assuming that the mixing matrix is fixed. However, the mixing matrix is commonly time-varying in practical communication system. In this paper, a model of gradually time-varying mixing system is proposed. Aiming at the model and the existing model of abruptly time-varying mixing system, a fast blind source separation algorithm is proposed by using the exponentially weighted sum of error squares as the cost function and adding the adaptive momentum term to the learning rule. Simulation results show that the proposed algorithm converges faster than existing algorithm and trace the time-varying system effectively.