Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze (PDRF). To solve the problem, the matched filter outputs of the PDRF under the action of target and jamming are analyzed. Then, the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF, and the time-frequency joint feature is constructed. Based on the time-frequency joint feature, the naive Bayesian classifier (NBC) with minimal risk is established for target and jamming recognition. To improve the adaptability of the proposed method in complex environments, an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed. The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio (SNR) decreases and the jamming-to-signal ratio (JSR) increases. Moreover, the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.