基于改进粒子滤波的微弱目标检测前跟踪算法

研究高速微弱目标的积累检测问题，提出了一种改进粒子滤波的检测前跟踪算法。该算法采用与雷达距离-多普勒图像相匹配的量测数据模型，能克服传统点扩散函数的模型误差。采用“新生”粒子从强度最高的分辨单元集内均匀产生，且按概率对权重最低的部分“存活”粒子用“新生”粒子将其替换的粒子更新策略，在增加粒子多样性的同时缓解了粒子的退化。仿真实验表明，本文算法的检测与跟踪性能要优于标准的粒子滤波算法。

Improved track-before-detect algorithm for weak targets based on particle filter

An improved track-before-detect (TBD) algorithm based on the particle filter is proposed for high-speed weak target detection. This method uses a unique measurement model for radar range-Doppler compression, which can effectively reduce the model error of the traditional sensor point spread function. An updating strategy is proposed that newborn particles are uniformly distributed within the set with high intensity bins and that the existing particles with low weights are replaced by new particles with probability. This strategy can improve the diversity among the particles and mitigate the effects of degeneracy. Simulation results show that the proposed algorithm has an improved performance of detecting and tracking dim targets compared with the standard particle filter.