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针对弹道中段弹头和碎片的微多普勒信息在多普勒谱中交缠重叠、难以分离与提取的问题,提出了一种基于完备总体经验模态分解(CEEMD)和改进自适应Viterbi算法相结合的多目标微多普勒信号分离与提取方法。通过分析进动弹头与旋转碎片微多普勒分布的差异性,对多目标回波信号进行CEEMD分解,结合小波阈值去噪方法,对各本征模态函数(IMF)进行分层处理并累加,分离出了弹头和碎片回波。对碎片信号进行了扩展处理,利用改进自适应Viterbi算法,抽取出相应的最优路径,实现多目标信号分离与微多普勒提取。仿真表明,该方法能有效克服多目标之间的干扰及噪声的影响,较好地实现了弹道多目标分离及微多普勒提取。
Aiming at the problem that the micro-Doppler information of warhead and debris in the middle of the ballistic trajectory is overlapped and overlapped in the Doppler spectrum and it is difficult to separate and extract the micro-Doppler information, a method based on complete general empirical mode decomposition (CEEMD) and modified adaptive Viterbi algorithm Combined multi-target micro-Doppler signal separation and extraction method. By analyzing the difference of the micro-Doppler distribution of projectile and rotating debris, the multi-target echo signals are decomposed by CEEMD, and the IMFs are layered by wavelet threshold denoising method , Separated warheads and debris echoes. The fragment signal is extended, the adaptive optimal Viterbi algorithm is used to extract the corresponding optimal path to achieve multi-target signal separation and micro-Doppler extraction. Simulation results show that this method can effectively overcome the interference between multiple targets and the influence of noise, and achieve the multi-target separation and micro-Doppler extraction well.