并行多参考模型卡尔曼滤波系统仿真研究

Simulation Study on Parallel Multiple-Model Kalman Filter Systems

研究了卡尔曼滤波器算法的基础上，提出随机系统多参考模型卡尔曼滤波器的新方法。该方法用N个线性模型近似表示参数变化很大的非线性系统。再用卡尔曼滤波器对N个模型进行滤波，得到N个状态的估计值。然后，对这N个状态估计值进行概率加权求和，得到最优状态估计值。分别针对二阶系统和船舶模型进行了大量的仿真研究。仿真结果展示，该方法具有广阔的工程应用前景。

： Based on a deep investigation on Kalman filters, this paper presents a parallel multiple-model Kalman filter method. The method utilizes a group of parallel linear models, for instance N models, to represent nonlinear systems. Kalman Filters associated with the group of models are used so that N estimated states are obtained. Then the N state estimates, each of which is weighted by its possibility that is also calculated on-line, are combined to form a optimal estimate. Through a lot of simulations for both a second-order system and a ship-control system, it demonstrated that the method is effective for systems with sharply changing parameters. Simulations also show that the method is applicable to a number of engineering problems for state estimates and parameter identifications.