多窗口谱图分析的低截获概率雷达信号识别

Low probability of intercept radar signal recognition based on multi-window spectrogram analysis

在当前复杂的战场环境中,低截获概率雷达信号因其具有大时宽带宽积、强干扰性能、高分辨率和低截获性特点得到了广泛应用,传统的雷达侦察手段很难对其进行有效识别。在低截获概率雷达典型调制分析的基础之上,研究基于人工智能的雷达信号分类识别方法。从低截获概率雷达信号时频特征入手,提出基于多窗口时频谱图分析方法。该算法采用Hermite函数作为谱图分析的窗函数,利用多个窗函数进行谱图分析,获得了聚集性更佳的有效信号,分散了噪声干扰,并且使信号调制特征的时频分析特征更加明显。在多窗口时频谱图基础上,采用迁移学习的思想,利用ImageNet-VGG-f神经网络完成信号的分类识别任务。实验结果表明,在低信噪比情况下,所提算法的性能优于传统的崔威廉姆斯分布和平滑伪维格纳分布方法。

In the current complex battlefield environment,low probability of intercept radar signal has been widely used due to their large time-bandwidth product,strong anti-jamming performance,high resolution and low interception.It is difficult to identify the low probability of intercept radar signal by traditional radar reconnaissance methods.Based on the analysis of typical modulation of low probability of intercept radar,a radar signal classification and recognition method based on artificial intelligence was studied.Starting from the time-frequency characteristics of low probability of intercepted radar signals,a multi-window spectrogram analysis method was proposed.In this algorithm,Hermite function was used as the window function of spectrum analysis,and multiple window functions were also used for spectrum analysis.The effective signal with better aggregation is obtained,the noise interference is dispersed,and the time-frequency analysis characteristics of signal modulation characteristics are more obvious through this algorithm.On the basis of multi-window spectrogram,the idea of transfer learning was adopted,and ImageNet-VGG-f neural network was used to complete the task of signal classification and recognition.Experimental results show that the performance of the proposed algorithm is better than the traditional Choi-William distribution and Smooth and Pseudo Wigner-Ville distribution methods at low signal-to-noise ratio.