河南理工大学学报：自然科学版
河南理工大學學報：自然科學版
하남리공대학학보：자연과학판
JOURNAL OF HENAN POLYTECHNIC UNIVERSITY
2012年
2期
201-206
,共6页
粒子滤波%自适应优化%退火参数%混合建议分布%多样性测度%重采样阂值
粒子濾波%自適應優化%退火參數%混閤建議分佈%多樣性測度%重採樣閡值
입자려파%자괄응우화%퇴화삼수%혼합건의분포%다양성측도%중채양애치
particle filter%adaptive optimization%anneal parameter%hybrid proposal distribution%diversity meas-ure%resampling threshold
通过对粒子滤波算法中建议分布与重采样2种改进技术分析，提出了一种粒子滤波自适应优化算法．首先，基于退火参数自适应优化混合建议分布，以改进建议分布的采样范围．然后，在基于有效样本大小的自适应重采样技术之上，借助另一多样性测度即种群多样性因子来自适应调整重采样阈值，而且，样本变异操作在重采样之后被引入确保样本的多样性．同时，结合部分分层重采样算法研究并进行改进，改进的部分分层重采样算法具有原算法执行快时间短的优点，同时结合权重优化的思想改进重采样的样本权重计算．通过仿真实验，粒子滤波自适应优化算法的性能和有效性均得以验证．
通過對粒子濾波算法中建議分佈與重採樣2種改進技術分析，提齣瞭一種粒子濾波自適應優化算法．首先，基于退火參數自適應優化混閤建議分佈，以改進建議分佈的採樣範圍．然後，在基于有效樣本大小的自適應重採樣技術之上，藉助另一多樣性測度即種群多樣性因子來自適應調整重採樣閾值，而且，樣本變異操作在重採樣之後被引入確保樣本的多樣性．同時，結閤部分分層重採樣算法研究併進行改進，改進的部分分層重採樣算法具有原算法執行快時間短的優點，同時結閤權重優化的思想改進重採樣的樣本權重計算．通過倣真實驗，粒子濾波自適應優化算法的性能和有效性均得以驗證．
통과대입자려파산법중건의분포여중채양2충개진기술분석，제출료일충입자려파자괄응우화산법．수선，기우퇴화삼수자괄응우화혼합건의분포，이개진건의분포적채양범위．연후，재기우유효양본대소적자괄응중채양기술지상，차조령일다양성측도즉충군다양성인자래자괄응조정중채양역치，이차，양본변이조작재중채양지후피인입학보양본적다양성．동시，결합부분분층중채양산법연구병진행개진，개진적부분분층중채양산법구유원산법집행쾌시간단적우점，동시결합권중우화적사상개진중채양적양본권중계산．통과방진실험，입자려파자괄응우화산법적성능화유효성균득이험증．
By analyzing two techniques, namely, proposal distribution and resampling, an adaptive optimiza- tion algorithm for particle filter is presented. Firstly, hybrid proposal distribution is adaptively optimized based on anneal parameter in order to improve the sampling range of proposal distribution, se＇condly, based on the a- daptive resampling techniques on effective sample size, auother diversity measure, namely population factor, is used to adaptively adjust the resampling threshold. Moreover, the particle mutation operation is integrated into PF after resampling so as to ensure the diversity of particle sets. finally, an improved partial stratified re- sampling （PSR） algorithm in PF is studied, which keeps the advantage of PSR in implementation speed and time and improves the pefrmance of PF with weight optimization. Throngh simulation experiments, validity of the proposed method is verified.
