Sparse classification and recognition method of fuzed targets and jamming signals

To enhance the frequency modulation radio fuze’s ability to prevent interference in the complex electromagnetic environment, a method of classifying and identifying the target and interference signals is suggested. This method is based on the sparse representation theory and uses the sparse representation coefficient reconstruction. The goal is to address the issue of the frequency modulation radio fuze’s insufficient ability to prevent interference. The output signals of the detector end of the radio fuze were collected, and the overcomplete dictionary of the target signal and the overcomplete dictionary of the interference signal were constructed. The test signals underwent sparse decomposition and reconstruction on both types of dictionaries. Based on the reconstruction errors, the test sample categories were determined. The results show that the classification and recognition method of frequency modulation radio fuze targets and interference signals based on sparse representation can effectively recognize targets and interference signals, and at the same time meet the lower false alarm probability. The research results have important reference significance for frequency modulation radio fuze’s anti-jamming and effective attack in complex electromagnetic environments.