基于时频原子方法的雷达辐射源信号特征提取

Feature extraction of radar emitter signals based on timefrequency atoms

针对电子战面临的高密集、信号形式复杂多变的雷达辐射源环境,提出一种全新的雷达辐射源信号特征提取方法。在过完备的时频原子库基础上,采用匹配追踪(MP)方法对信号进行时频原子分解,并通过改进的量子遗传算法(IQGA)降低MP搜索过程的时间复杂性,得到表示雷达辐射源信号特征信息的最佳时频原子,为后期的辐射源信号分选和识别进行特征准备。实验结果证实了该方法的有效性和可行性,所提取的时频原子特征具有一定的抗噪性能。

A novel approach to extract the features of radar emitter signals in the high density and complex and variable signal modulation environment is presented in this paper. Based on the over-complete time-frequency atom dictionary, the signals are decomposed into a linear expansion of atoms by the method of Matching Pursuit. Then, improved quantum genetic algorithm is applied to effectively reduce the time-complexity at each search step of MP, and thus some optimal time-frequency atoms describing features of signals are obtained, which can provide some new feature parameters for the deinterleaving and recognition of the radar emitter signals subsequently. Experiment result proved the validity and feasibility of the approach and that the extracted atoms had the features of a certain extent noisesuppression ability.