基于RobustICA的数字调制混合信号盲源分离算法

针对含噪环境下数字调制混合信号盲源分离(BSS)误码率(BER)过高的问题,提出了一种基于RobustICA的二阶段盲源分离算法R-TSBS。该算法采用RobustICA算法对阵列响应向量构成的混合矩阵进行估计,然后利用数字调制信号的有限符号集特征,在第二阶段用最大似然估计(MLE)方法估计各个数字调制源信号发送的符号序列,达到盲源分离的目的。实验仿真表明,传统的独立成分分析(ICA)算法如RobustICA算法和FastICA算法误码率很高,在信噪比(SNR)为10 dB时,其误码率达到了3.5×10^-2左右,而基于FastICA的二阶段盲源分离算法F-TSBS和基于RobustICA的二阶段盲源分离算法R-TSBS的误码率则下降到了10^-3,分离性能得到了明显改善;在较低的信噪比(0~4 dB)下,R-TSBS算法较F-TSBS算法约有2 dB性能提升。

Blind source separation method for mixed digital modulation signals based on RobustICA

Since the Bit Error Rate (BER) of the Blind Source Separation (BSS) of mixed digital modulation signals under the noisy environment is excessively high, a two-stage blind source separation algorithm named R-TSBS was proposed based on RobustICA (Robust Independent Component Analysis). Firstly, the algorithm used RobustICA to estimate the mixing matrix consisting of array response vector. In the second phase, each symbol sequence transmitted by digital modulation source signal was estimated by Maximum Likelihood Estimation (MLE) method using the finite symbol values character. Finally, R-TSBS achieved the purpose of blind source separation. The simulation results show that, when the Signal to Noise Ratio (SNR) is 10 dB, the BER of traditional Independent Component Analysis (ICA) algorithm such as FastICA (Fast Independent Component Analysis) and RobustICA reached 3.5×10^-2, which is exactly high. However, the BER of the two-stage blind source separation on the basis of FastICA algorithm which named F-TSBS and the proposed R-TSBS algorithm dropped to 10^-3, the separation performance has been significantly improved. At the same time, R-TSBS algorithm can obtain about 2 dB performance increase in low SNR (0~4 dB) compared to F-TSBS algorithm.