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.

本文考虑脉冲多普勒雷达引信（PDRF）的目标和干扰识别问题。针对该问题，分析了PDRF在目标和干扰作用下的匹配滤波器输出。然后，从PDRF的匹配滤波器输出中提取频率熵和峰峰值比，构建时频联合特征。基于时频联合特征，建立风险最小的朴素贝叶斯分类器（NBC）用于目标和干扰识别。为了提高所提方法在复杂环境中的适应性，提出了一种在 PDRF 工作期间自适应地修改分类器的在线更新过程。实验表明，当信噪比（SNR）降低和干扰信号比（JSR）增加时，PDRF可以保持较高的识别精度。此外，应用分析表明，ONBCMR方法计算复杂度低，完全可以满足PDRF的实时性要求。