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中文摘要：
近年来序列蒙特卡罗理论及其应用在自动导航、非线性估计与金融等诸多领域受到了越来越广泛的关注。提出了一种引入残差信息的分层重采样策略,通过引入当前粒子集权值的残差来构建累积分布函数,同时针对随机区间逐级分层以产生有序的随机数集合,从而提高重采样的合理性与采样效率。首先从仿真实验的角度证明了它的有效性,对比残差重采样、多项式重采样与遗传重采样,提出的重采样策略在后验均值误差、均方差与运行时间方面均为最小;将提出的重采样策略嵌入到运动目标跟踪算法中,基于标准测试视频的跟踪结果同样佐证了该重采样策略的收敛性及良好的抗噪性能。
英文摘要：
Recently,sequential Monte Carlo theory has been applied abroad in different domains such as self-determined navigation,non-linear estimation and finance,and it attracts researchers more and more.A stratified resampling strategy is brought forward imported by residual information in this work.Accumulative distributing function is constructed by importing residu-als of particles＇weights.Synchronously,sequential stochastic numbers are gradually produced by arranging on stochastic muster.The way improves the rationality and efficiency of sampling strategy.The method is confirmed by experiment based on emulational program.Compared with residual resampling strategy,polynomial resampling strategy and genetic resampling strategy,the systemic errors about resampling strategy are lowest in posterior mean error,mean square error and running time.The improved resampling strategy is embedded in object tracking algorithm.The correlative results show astringency and antinoise-capability about sampling strategy are excellent on data based on standard testing video.
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