基于自适应视野聚类匹配的多目标分离与提取

针对雷达多目标回波微多普勒信息复杂交叠、难以分离与提取的问题,提出了一种基于自适应视野聚类和Viterbi算法相结合的多目标信号分离与提取方法。该方法在时频分析的基础上,利用各旋转目标散射点不同的微多普勒变化特性,进行自适应视野处理,获取各时刻视点在不同视野范围内的食物浓度序列,通过聚类分析获得0-1编码序列,并结合Viterbi算法进行配准处理,得到最优匹配路径,从而实现多目标信号分离与提取。仿真结果表明,该方法能够有效地克服交叉区域干扰及背景噪声的影响,适用于复杂散射模型,较好地实现了微动多目标信号分离及提取。

Multi-target separation and extraction based on adaptive vision cluster matching

Aiming at the complexity and intersection of separation and extraction on radar multi-target echo, an algorithm based on the adaptive vision cluster and Viterbi algorithm is proposed. Based on time frequency analysis, the adaptive vision processing is firstly implemented by analyzing the change trend of micro Doppler information. Food concentration sequence in different field of vision scope is obtained in every time. Then 0-1 code sequences are gained by cluster. Meanwhile, 0-1 code sequences are registered with the Viterbi algorithm to obtain optimal matching path. The multi-target separation and extraction is implemented. The simulation result indicates that the proposed method can overcome the interference of intersection region and noise, and is suitable for the complex scattering model. Thus the proposed method is implementing the multi-target resolution and micro Doppler extraction well.