分数阶傅里叶和压缩感知自适应抗频谱弥散干扰

频谱弥散(SMSP)干扰与线性调频雷达信号之间存在大量的时频域耦合，干扰效能突出。该文提出一种信息域的抗SMSP干扰的信号处理算法，根据SMSP干扰信号的形式与特点，通过自适应改变压缩感知的干扰基字典，同时匹配雷达信号与干扰信号的调频率，构建压缩感知求解模型并基于凸优化算法完成信号重构，最终实现干扰信号的识别及雷达信号的提取。该算法中冗余字典的构造采用了Pei型分数阶傅里叶快速分解方法，不需要反复对信号进行时频域解耦，并且迭代次数较少，运算效率较高。

Fractional Fourier Transform and Compressed Sensing Adaptive Countering Smeared Spectrum Jamming

SMeared SPectrum (SMSP) jamming has lots of coupling in time and frequency domain with Linear Frequency Modulated (LFM) radar signals, which has good jamming performance. This paper proposes a signal processing method for countering SMSP jamming in information domain. According to the formulation and characteristics of SMSP signal, the jamming dictionary is changed automatically, the frequency modulation rate of LFM and SMSP signal is matcheal at the same time, the compressed sampling model is consructed and reconstruction of signal is carried out based on convex optimization. Finally, the recognition of jamming signal and extraction of radar signal are achieved. Pei type fractional Fourier decomposition method is used in construction of redundant dictionary. Modulation and demodulation between time and frequency domain are avoided in this method, which leads to improvement in fewer iteration times and higher arithmetic speed.