基于 BAS-BPNN 的调频无线电引信目标与扫频干扰识别方法

In order to solve the problem that the FM radio fuze is unable to counter AM frequency sweep jamming signals on battlefield in a complex electromagnetic environment, a target recognition method based on frequency domain information entropy, norm entropy and cepstrum entropy is proposed. Based on the output signal of the FM radio fuze under the action of the target and AM frequency sweep jamming signal, the frequency information entropy,norm entropy and cepstrum entropy are extracted to construct the feature matrix. The BAS algorithm is used to optimize the initial weight values and threshold of the back propagation neural network (BPNN). Then the optimized BPNN is used to classify and recognize the target and AM frequency sweep jamming signal. The experimental results with the measured data show that the feature matrix formed by feature extraction has separability between the target and the jamming signal. When the BPNN with optimal parameters is obtained by the optimization of the BAS algorithm, the recognition accuracy of the classifier can reach 99- 96%, which significantly improves the ability of the FM radio fuze to counter AM frequency sweep jamming signals.