ADAPTIVE MULTIPLE MODEL FILTER USING IMM AND STF

• 作 者: LIANG Yan PAN Quan ZHOU Dong-h
• 机 构: Dept. of Automatic Control
• 出 处: 航空学报（英文版）
• 发表日期: 2004年期
• 关键词: tracking maneuvering targets interacting multiple model adaptive filtering Kalma
• 基金项目: 国家自然科学基金

In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least-Squared Estimation. In hybrid estimation, the well-known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter - SIMM. In this filter, our modified STF is a parameter-adaptive part and IMM is a mode-adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model-conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.