复杂体制雷达辐射源信号时频原子特征提取方法

A Novel Approach of Feature Extraction for Advanced Radar Emitter Signals Using Time-Frequency Atom Decomposition

针对复杂体制雷达辐射源信号调制类型识别问题,提出一种新的辐射源信号脉内时频原子特征提取方法(TFAD).该方法首先利用稀疏分解原理和改进差分进化算法将辐射源信号在Ga-bor和Chirplet时频原子库中进行分解,然后利用分解后的首原子能量和Gabor原子中心频率参数分别提取出2个相似比特征和1个频率方差特征作为辐射源信号脉内调制类型的分类特征,最后通过构造有向循环图支持向量机分类器实现雷达辐射源信号的分类识别.与计算复杂度至少为O(nlogn)的分形方法相比,TFAD方法只有O(n)的计算复杂度.采用不同信噪比和多种调制参数的5种辐射源信号进行大量仿真实验,结果表明TFAD方法可获得98.3%的平均正确识别率.

A novel approach, called TFAD, is proposed to extract time-frequency atom features to effectively recognize the intra-pulse modulation types of advanced radar emitter signals. The method decomposes emitter signals based on matching pursuit in Gabor and Chirplet atom dictionaries using a modified differential evolution algorithm. Then both the energy of the first decomposed atoms and the frequency parameters of the decomposed Gabor atoms are utilized to extract two correlation ratio features and a frequency variance feature. These features are used as the classification features to recognize different intra-pulse modulations of emitter signals by constructing a directed acyelic graph support vector machine classifier. The computational complexity of TFAD is O（n）, while the computational complexity of the fractal approach is O（nlogn）. Simulation results conducted on various signal-to-noise ratios and a wide range of modulation parameters of five typical radar emitter signals show that TFAD achieves an average correct recognition rate of 98. 3%.