针对雷达如何实施智能抗欺骗干扰问题,提出了基于时频图像色度差异与深度随机森林的雷达智能抗欺骗干扰方法。首先,对雷达接收信号进行时频变换,经灰度处理后,将RGB三维数据信息转换为一维信息,利用全局阈值对原始时频图分割处理;其次,对分割后的图片提取颜色分量特征,利用深度随机森林(deep random forest,DRF)进行干扰识别;最后,基于识别结果及真实信号的位置信息设计二维时频滤波,从而将干扰与噪声滤除。该算法不必对信号参数进行估计,计算复杂度低。仿真结果表明,干信比(JSR)大于3 dB和小于-2 dB时干扰识别率均能达到90%以上,而在干扰与信号功率相当时,可通过增加盒维数与信息熵等特征来进一步提高识别率。

Aiming at the problem of how radar implements intelligent anti-deception jamming, a radar intelligent anti-deception jamming method based on the time-frequency image chromaticity difference and the deep random forest is proposed. Firstly, perform the time-frequency transformation on the radar received signal, and after gray-scale processing, convert the RGB three-dimensional data information into one-dimensional information, and use the global threshold to segment the original time-frequency imag. Secondly, extract the color component features of the segmented image, then use deep random forest (DRF) for interference identification. Finally, design a two-dimensional time-frequency filter based on the identification result and the location information of the real signal to filter out interference and noise. The algorithm does not need to estimate the signal parameters, and the computational complexity is low. The simulation results show that the interference recognition rate can reach more than 90% when the jamming signal ratio (JSR) is greater than 3 dB and less than -2 dB, and when the jamming is equivalent to the signal power, the recognition rate can be further improved by adding features such as box dimension and information entropy.