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.

A method for active deception jamming discrimination based on frequency response

There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve the above problem,this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes.Firstly,in the range-doppler domain,the real and false targets are truncated along the range dimension,and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response.Then,a two-channel feature fusion network is designed for real and false target recognition.Finally,a Multi-Coherent Processing Interval Joint Decision Method(M-CPIJDM)based on temporal information is proposed to improve the recognition performance.Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds:distance false target,velocity false target,distance-velocity composite false target,and forwarding dense false target.