自动化学报2016，Vol.42Issue(4)：535-544,10.DOI:10.16383/j.aas.2016.c150486
具有双重不确定性系统的联合滤波算法
A New Combined Filtering Algorithm for Systems with Dual Uncertainties
摘要
Abstract
Kalman filter is optimal under the assumption of Gaussian white noise, while the set-membership filter (SMF), which is based on interval mathematics, can deal with bounded noise efficiently. However, in many situations, the actual control system is usually interrupted by both random noises and bounded noises simultaneously. It is not easy to obtain expected results by using only one single filter, due to the limited application fields of the two filtering algorithms. In this paper, according to the established system model with dual uncertainties, a new kind of filter named combined filter is proposed, which is based on Bayesian estimation. This algorithm can deal with random uncertainties by applying Kalman filter, and can deal with bounded uncertainties by applying set-membership filter. Accordingly, a new kind of easy filter is produced. The effectiveness of the new filtering algorithm is verified in a radar tracking simulation system. From the simulation results, the combined filter algorithm can produce better adaptability and effectiveness than any one single filter.关键词
卡尔曼滤波/集员滤波/双重不确定性/联合滤波Key words
Kalman filter/set-membership filter (SMF)/dual uncertainties/combined filter引用本文复制引用
江涛,钱富才,杨恒占,胡绍林..具有双重不确定性系统的联合滤波算法[J].自动化学报,2016,42(4):535-544,10.基金项目
国家自然科学基金(61273127,61473222,61533014),航天器在轨故障诊断与维修实验室开放课题(SDML OF2015004),陕西省科技创新团队(2013KCT-04)资助Supported by National Natural Science Foundation of China (61273127,61473222,61533014), the Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit (SDML OF2015004), and Innovative Research Team of Shaanxi Province (2013KCT-04) （61273127,61473222,61533014）
