基于功率谱熵的无线电引信目标与干扰信号分类方法

Radio frequency modulation fuze is easy to be disturbed by jamming signals in a battlefield environment, which lead to explosion early and loss of attack ability. In a combat setting, jamming signals can easily disrupt radio frequency modulation fuses, resulting in an early explosion and a loss of assault capability. In order to identify target and jamming signals accurately, a classification method based on signal power spectrum entropy is proposed. Using the measured output signals of radio fuze, the power spectrum exponential entropy and Renyi entropy of the target and jamming signals are extracted to form feature vectors, which is used as the input of KNN classifier to classify target and jamming signals, and verified by 5-fold cross validation method. The target and jamming signals' power spectrum exponential entropy and Renyi entropy are extracted from the radio fuze's measured output signals to create feature vectors. These vectors are then fed into a K-nearest neighbor (KNN) classifier to classify the target and jamming signals, and their classification is confirmed through the use of the 5-fold cross validation method.The results show that there is a significant difference between the power spectrum exponential entropy and Renyi entropy of the target and jamming signals, and the highest classification accuracy reaches 99.47% when the KNN classifier is used to classify the target and jamming signals.