基于小波系数相关性的间歇采样转发干扰识别

Interrupted-Sampling and Repeater Jamming Recognition Based on Wavelet Coefficient Correlation

干扰识别已成为认知雷达重要组成部分,针对数字射频存储(Digital Radio Frequency Memory,DRFM)干扰机的间歇采样转发干扰识别问题,提出一种基于小波系数相关性的识别算法。利用小波高频系数在时域上具有高分辨率的特点,首先对干扰信号进行小波分解,其次根据间歇采样原理和规律,对小波系数序列进行移位相关计算,最后将相关系数作为特征量进行分类识别。构建不同参数的信号样本对算法的识别准确率进行仿真,仿真结果表明,在信噪比(Signal-to-Noise Ratio,SNR)较高(SNR>0 dB)时,识别准确率近似为100%,在低信噪比处(SNR=-10 dB)仍有不小于80%的正确率,同时叠加回波后也可以有效识别干扰,因此证明了算法的有效性,具有一定工程实用价值。

Interference identification has become an important part of cognitive radar.For the current problem of interrupted-sampling and repeater jamming recognition using digital radio frequency memory(DRFM)jammer,a recognition algorithm based on wavelet coefficient correlation is proposed.Considering that the wavelet high-frequency coefficients have high resolution in time domain,the interference signal is decomposed by wavelet.According to the principle of interrupted-sampling,the shifting correlation of wavelet coefficient series are calculated.The correlation coefficient is used as the feature for classification and recognition.Signal samples with different parameters are constructed to simulate the recognition accuracy of the algorithm.Simulation results show that the recognition accuracy rate is approximately 100%with higher signal to noise ratio(SNR)(SNR>0 dB).The accuracy rate is still not less than 80%at low signal to noise ratio(SNR=-10 dB).Jamming can also be effectively identified after stacking echo.The effectiveness of the algorithm is proved.It has certain engineering practical value.