针对基于粒子滤波盲分离算法运算复杂度高的问题，将支持向量机理论引入到粒子滤波算法中，提出一种数字调制混合信号单通道盲分离算法. 确定了算法中粒子权值更新方法，并给出了参数与符号序列的后验概率密度估计. 从后验概率密度估计的统计特性和运算复杂度两个方面详细分析了算法的性能，并从理论上证明该文给出的后验概率密度估计能很好地逼近真实后验概率密度. 理论分析与仿真结果均表明，与基于粒子滤波的盲分离算法相比，算法在误码率性能相当的情况下，有效缩短了运算时间.

 To reduce computation complexity in the particle-filtering based blind source separation, a novel eparation algorithm of co-frequency digital modulated mixture is proposed, which combines the particle filtering lgorithm with support vector machine. Formulae for assigning particle weights and expression for stimating a posterior distribution function are derived. Performance of the proposed algorithm is analyzed in wo aspects: statistic characteristics of the estimated posterior density function and its computation complexity. he estimated posterior density function is shown to be close to the true function. Both the theoretical nalysis and simulation results show that the proposed algorithm can reduce processing time without loss of
performance in terms of bit error probability as compared with the particle-filtering based blind source separation lgorithm.