Research of Optimized Adaptive Kalman Filtering

摘要：

　　Standard Kalman Filtering leads to divergence because of inaccurate system model and noise statistic.Researchers have taken relative studies about Kalman filtering optimization method.But now most studies are based on applications,such as integrated navigation system,so most of these methods are lack of general applicability.This paper starts from innovation-based adaptive estimation(IAE)filtering and memory attenuated(MA)filtering.These two optimized filtering methods have respective advantages and disadvantages.We combined them to create a new optimal filtering,namely,Optimized Adaptive Kalman Filtering(OAKF).New method gained the attenuation factor values in real time by innovation analysis to control the effect of filtering.Software simulation shows the new optimized adaptive Kalman filterings good effect in different conditions.After compared with the standard Kalman filtering,IAE filtering and MA filtering,OAKF methods has better filtering effect than other methods.

关键词： Innovation-based adaptive estimation (IAE) Memory Attenuated (MA) Kalman filtering filtering divergence Information Fusion

作者: XU Fuzhen SU Yongqing LIU Hao

作者单位: School of Electronics and Information,Tongji University,Shanghai 201804

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 1210-1214

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）