井冈山大学学报（自然科学版）
井岡山大學學報（自然科學版）
정강산대학학보（자연과학판）
JOURNAL OF JINGGANGSHAN UNIVERSITY(SCIENCE AND TECHNOLOGY)
2015年
4期
49-54
,共6页
扩展卡尔曼滤波%限定记忆滤波%旧量测数据%自适应算法%系统突变状态
擴展卡爾曼濾波%限定記憶濾波%舊量測數據%自適應算法%繫統突變狀態
확전잡이만려파%한정기억려파%구량측수거%자괄응산법%계통돌변상태
extended Kalman filtering%limit memory filtering%old measurements%adaptive filtering%mutant system state
为解决扩展卡尔曼滤波器（EKF）鲁棒性差，且无法实时精确跟踪系统突变状态的问题，研究一种基于限定记忆滤波的自适应EKF算法。算法将EKF与限定记忆滤波器相融合，减小旧量测数据对滤波效果的影响，提高估计精度；引入自适应因子与渐消因子，通过实时调节新旧滤波增益阵以及预测状态值，精确地跟踪系统突变状态。仿真实例表明，强跟踪算法与经典EKF算法相比，自适应EKF算法鲁棒性好，滤波精度高，能够有效地跟踪系统突变状态。
為解決擴展卡爾曼濾波器（EKF）魯棒性差，且無法實時精確跟蹤繫統突變狀態的問題，研究一種基于限定記憶濾波的自適應EKF算法。算法將EKF與限定記憶濾波器相融閤，減小舊量測數據對濾波效果的影響，提高估計精度；引入自適應因子與漸消因子，通過實時調節新舊濾波增益陣以及預測狀態值，精確地跟蹤繫統突變狀態。倣真實例錶明，彊跟蹤算法與經典EKF算法相比，自適應EKF算法魯棒性好，濾波精度高，能夠有效地跟蹤繫統突變狀態。
위해결확전잡이만려파기（EKF）로봉성차，차무법실시정학근종계통돌변상태적문제，연구일충기우한정기억려파적자괄응EKF산법。산법장EKF여한정기억려파기상융합，감소구량측수거대려파효과적영향，제고고계정도；인입자괄응인자여점소인자，통과실시조절신구려파증익진이급예측상태치，정학지근종계통돌변상태。방진실례표명，강근종산법여경전EKF산법상비，자괄응EKF산법로봉성호，려파정도고，능구유효지근종계통돌변상태。
In order to solve the problem that Extended Kalman filtering (EKF) cannot tracking mutant system state accuracy and improve the robustness of the filtering. A new adaptive Kalman filtering (AEKF) based on limited memory has been proposed. This algorithm combines EKF with Limit memory filtering that minimizes the influence of the old measurements. Forgetting factor and weakening factor can track mutant state accuracy by the technology of dynamically adjusting the weight of state prediction and Kalman gains in the filter estimation. The results of simulation experiments demonstrate that in comparison with Strong Tracking Filtering and Extended Kalman Filtering, AEKF provides higher estimated accuracy and better .robustness to track mutant state.
