一种基于独立分量分析的变速跳频信号盲分离方法

针对当前跳频信号盲分离算法计算量大，精确度不高的问题，结合变速跳频信号采用不断加快的跳速和“跳速多变”的策略，提出了一种利用信源间的独立性解决变速跳频信号盲分离问题的方法。同时，采用负熵最大化寻优算法加快了传统独立分量分离算法运算速度。通过仿真实验与处理实际数据结果表明：与其他方法相比，该方法在不需要任何先验信息的条件下，可以在低信噪比的情况下较好地分离出各个变速跳频信号，同时能够精确恢复出变速跳频信号的跳频图案，在20 dB信噪比的情况下，分离后相似系数可以达到99%。该研究为变速跳频信号盲分离问题提供一个新的解决途径。

A Blind Separation of Variable Frequency Hopping Signals Based on Independent Component Analysis

Aimed at the problems that in the face of the increasingly complex electromagnetic environment, the feature recognition algorithm for the blind source separation of multi frequency hopping signals is heavy in computation, and the separation result is inaccurate, in combination with the variable speed frequency hopping signal, a strategy of accelerating the hopping speed and varying the hopping speed is adopted. Simultaneously the independent component analysis method is utilized for dealing with the blind separation problem of variable speed frequency hopping signals, and the negative entropy maximization algorithm is used to accelerate the separation speed of traditional independent components. The simulation results and actual frequency hopping data show that compared with other methods this algorithm can effectively separate the multiple variable speed frequency hopping signals without any prior information and low SNR. At the same time, the time domain waveform of the variable frequency hopping signal and the corresponding frequency hopping pattern can be accurately recovered, in the case of 20 dB SNR, the separation similarity coefficient can reach 99%. The analysis mentioned above provides a new solution for the blind separation problem of the variable frequency hopping signal.