中国惯性技术学报
中國慣性技術學報
중국관성기술학보
JOURNAL OF CHINESE INERTIAL TECHNOLOGY
2013年
1期
84-88
,共5页
粒子滤波%Sage-Husa滤波%自适应Sage-Husa粒子滤波%SINS/SAR组合导航
粒子濾波%Sage-Husa濾波%自適應Sage-Husa粒子濾波%SINS/SAR組閤導航
입자려파%Sage-Husa려파%자괄응Sage-Husa입자려파%SINS/SAR조합도항
particle filtering%Sage-Husa filtering%adaptive Sage-Husa particle filtering%SINS/SAR integrated navigation
针对非线性滤波问题,提出一种新的自适应Sage-Husa粒子滤波算法.通过Sage-Husa滤波方法计算状态估值和协方差阵来获得重要性密度分布函数,充分考虑了最新量测信息的影响,并利用欧氏距离和反映量测噪声统计特性的精度因子自适应地调整粒子权值的分布,降低粒子退化程度,提高了滤波精度,适用于非线性非高斯系统模型的滤波问题.将提出的算法应用于SINS/SAR组合导航系统中,与扩展 Kalman 滤波和粒子滤波比较,仿真结果表明,自适应 Sage-Husa 粒子滤波能提高导航系统定位的解算精度,得到的东向和北向定位误差控制在5.3 m±附近,其性能明显优于扩展 Kalman滤波和粒子滤波.
針對非線性濾波問題,提齣一種新的自適應Sage-Husa粒子濾波算法.通過Sage-Husa濾波方法計算狀態估值和協方差陣來穫得重要性密度分佈函數,充分攷慮瞭最新量測信息的影響,併利用歐氏距離和反映量測譟聲統計特性的精度因子自適應地調整粒子權值的分佈,降低粒子退化程度,提高瞭濾波精度,適用于非線性非高斯繫統模型的濾波問題.將提齣的算法應用于SINS/SAR組閤導航繫統中,與擴展 Kalman 濾波和粒子濾波比較,倣真結果錶明,自適應 Sage-Husa 粒子濾波能提高導航繫統定位的解算精度,得到的東嚮和北嚮定位誤差控製在5.3 m±附近,其性能明顯優于擴展 Kalman濾波和粒子濾波.
침대비선성려파문제,제출일충신적자괄응Sage-Husa입자려파산법.통과Sage-Husa려파방법계산상태고치화협방차진래획득중요성밀도분포함수,충분고필료최신량측신식적영향,병이용구씨거리화반영량측조성통계특성적정도인자자괄응지조정입자권치적분포,강저입자퇴화정도,제고료려파정도,괄용우비선성비고사계통모형적려파문제.장제출적산법응용우SINS/SAR조합도항계통중,여확전 Kalman 려파화입자려파비교,방진결과표명,자괄응 Sage-Husa 입자려파능제고도항계통정위적해산정도,득도적동향화북향정위오차공제재5.3 m±부근,기성능명현우우확전 Kalman려파화입자려파.
Aiming at the nonlinear filtering problem, this paper proposes an improved adaptive Sage-Husa particle filtering algorithm by using Sage-Husa filtering to obtain state estimation and covariance, thus it provides a reliable importance density function that considers the latest measurement information. Then the Euclidean distance and accuracy factor constructed from statistic performance of measurement information can adaptively regulate the weight function. Thus it is more suitable for filtering calculation based on nonlinear and non-Gaussian models, through preventing the particles from degeneracy and increasing the precision of filtering. By applying the proposed algorithm into SINS/SAR integrated navigation system and comparing with extended Kalman filtering and particle filtering, the experiments demonstrate that east and north position error of adaptive Sage-Husa particle filtering are within 5.3 m± respectively, and it outperforms the extended Kalman filtering and particle filtering in terms of accuracy, thus improving the calculation precision in navigation system.