具有参数自适应的交互式多模型算法

动态多模型估计（SMME）广泛应用于结构和参数的不确定／变化的估计问题中，比如目标跟踪和故障诊断与隔离，然而由先验信息选定的滤波参数是模式切换与模式未切换情况下的折衷，针对SMME，本文通过在每个滤波循环开始处起始多个状态预测器实时辨识滤波参数，包括模式切换概率和基于模型的过程噪声方差，考虑到交互式多模型（IMM）是SMME中比较有效的方法，我们将上述的参数辨识与IMM相结合，提出了一种自适应IMM（AIMM），在跟踪一个机动目标的仿真中，AIMM表现出了比IMM更高的估计精度。

Parameter Identification in Switching Multiple Model Estimation and Adaptive Interacting Multiple Model Estimator

Switching multiple model estimation (SMME) has been widely applied in problems with both structural and parametric uncertainties and/or changes, ranging from target tracking to fault detection and isolation. However its filtering parameters, determined by a priori information, are the tradeoff between the "mode transition" case and the "non mode transition" case. Hence an online method for SMME to identify filtering parameters, including Markov transition probabilities and the variances of model conditional process noise, are proposed, by using additional multiple state predictors at the beginning of each filtering cycle. By combining the parameter identification with interacting multiple model (IMM), which is one of the most cost effective estimators in SMME, we present an adaptive IMM (AIMM), which shows much more accurate than IMM in the simulation of tracking a maneuvering target.