适用于时变不确定系统的并行模型自适应估计

Parallel-Model Adaptive Estimation for Time-Varying Uncertain Systems

针对受到潜在模型不确定性影响的系统,设计一种并行模型自适应估计(PMAE)算法.以往基于不确定性系统模型设计的滤波算法,在模型精确的情况下,性能往往不及传统卡尔曼滤波(KF).为了解决该问题,设计基于多个并行滤波器的自适应状态估计算法,其中一个滤波器为KF,用于在未出现模型不确定性的情况下,对系统进行最优状态估计;另一个滤波器为扩维卡尔曼滤波(AKF),用于在出现模型不确定性的情况下,对不确定性模型参数进行辨识.以空间目标监视为例,分析算法的性能.仿真结果表明,利用PMAE算法能够自适应地对两个并行滤波器进行切换和折衷,从而有效应对模型中存在不确定性和不存在不确定性两种情况.

An parallel-model adaptive estimation（ PMAE） algorithm is presented for a time-varying system where model uncertainty may occur occasionally. Generally,an algorithm which is designed for an uncertain system may yield sub-optimal performance when the model uncertainty does not occur. To cope with this problem,we propose to use two filters in parallel in a multiple-model framework. One of the filters,an augmented Kalman filter（ AKF）,provides estimates of uncertain parameters when the model uncertainties occur,whereas the second filter,a Kalman filter（ KF）,yields high precision in the absence of the uncertainties. A space surveillance example is given in simulation to show the potential application of the presented algorithm. It indicates that the PMAE is efficient to deal with model uncertainty.