基于负熵最大化的卫星测控信号盲识别算法

Blind Recognition Algorithm of Telemetry,Track and Control Signals of Satellite Based on Negentropy-maximization

针对卫星测控通信信息侦察中测控信号盲识别的问题,提出一种基于负熵最大化的测控信号副载波识别算法。首先介绍了独立分量分析的基本理论,根据独立分量分析求解问题的思路,由互信息准则得出能够表征输出信号之间独立性的目标函数即负熵。由于源信号是盲信号,源信号的概率密度未知,采用负熵的近似表达式来计算,最大化负熵代表着输出信号之间互相独立,即实现了信号的分离。在深入分析基于负熵最大化的快速独立分量分析算法的基础上,将其用于卫星测控信号的盲识别上。MATLAB仿真结果表明,该识别算法可以较好地分离卫星测控信号,具有良好的稳定性,收敛速度较快。

A blind sub-carrier recognition algorithm of telemetry, track and control （TT＆C） in satellite system is proposed based on negentropy-maximization in terms of recognition of TT＆C signals for military TT＆c information scout in satellite system. The basic principle of the independent component analysis（ICA） is discussed. According to the problem-solving ideas of ICA, an objective function is presented which shows independence of output signals based on mutual information theory. As the source signal is blind, the probability density of source signals is unknown. To separate the signals, negentropy-maximization is achieved with approximate entropy of negentropy that represents the independence of the output signals. Based on analyzing FastICA algorithm, this paper expounds a new method to adopt it in the recognition of TT＆C signals of satellites. Simulation results in MATLAB show its better performance and efficiency in the mixed Tt＆c signals of satellite recognition, proving its good convergence and robustness.