A new likelihood approach to autonomous multiple model estimation.
A new likelihood approach to autonomous multiple model estimation.
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DOI：
10.1016/j.isatra.2019.09.005
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发表时间：
2020-04
期刊：
影响因子：
7.3
通讯作者：
H. Soken;S. Sakai
中科院分区：
文献类型：
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作者：
H. Soken;S. Sakai
针对参数突变的混合系统，提出了一种自治多模型（AMM）估计算法。卡尔曼滤波器（KF）的调整和采用不同的系统模式的估计合并的基础上，一个新定义的似然函数，而没有任何必要的过滤器的相互作用。建议的似然函数是由两个措施，滤波器的敏捷性措施和稳态误差措施。这些措施的基础上得出的过滤器自适应规则。数值结果表明，所提出的算法，所谓的竞争AMM（CAMM），既保证稳态估计精度和快速参数跟踪。
This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules. The numerical results show that the proposed algorithm, so called Competing AMM (CAMM), guarantees both steady-state estimation accuracy and quick parameter tracking.