基于模糊c-均值增量更新的脉冲多普勒引信干扰与目标信号识别

针对脉冲多普勒（PD）引信在复杂战场电磁环境中对目标回波信号识别效率不足问题，提出了一种具有自适应增量更新功能的模糊c-均值(FCM)聚类算法，以有效地提高引信对干扰和目标信号的识别率。在对引信距离门选通输出信号分析的基础上，利用信号时域与频域熵特征，借助FCM算法对干扰与目标信号进行分类识别，并针对信噪比不断恶化的情况，通过改进的增量更新算法实现引信FCM分类模型自适应更新调整，从而保持引信对干扰和目标信号的高识别率。仿真实验结果表明：改进的增量更新算法在降低更新耗时的同时，可使引信在-15 dB信噪比条件下对干扰与目标信号识别正确率达到96.43%，显著地提高了PD引信抗有源噪声干扰能力。

Recognition of Jamming and Target Signal for Pulse Doppler Fuze Based on FCM Algorithm with Incremental Update

A fuzzy c-means (FCM) method with self-adaptive update function is proposed to improve the target recognition accuracy of pulse Doppler (PD) fuze in the complex electromagnetic environment. Based on the analysis of the output signal from fuze range-gate, the time domain and frequency domain entropies of signal are used for the recognition of target signal and jamming by means of FCM method. A modified incremental update algorithm is used to update and adjust the FCM classification model when the signal-to-jamming ratio decreases. The simulated results indicate that the modified incremental update algorithm can not only reduce the time-consuming, but also keep the recognition accuracy of target signal and jamming at 96.43% at -15 dB SNR, which significantly improves the anti-active noise jamming ability of pulse Doppler fuze.