基于H_∞滤波器的交互多模型算法

Interacting multiple model algorithm based on H_∞ filter

为了解决在实际情况中噪声的统计特征信息经常残缺甚至完全未知的问题,在量测噪声的协方差未知的前提下,提出了一种方法解决含有马尔可夫参数线性离散系统的估计问题.在交互多模型算法的基础上融合H_∞滤波器构造估计器,得到交互过程中必需的噪声统计特征,进而对目标的状态进行估计.该方法不但解决了噪声信息缺失问题,而且保留了交互多模型算法的优异性能.通过与单模型H_∞滤波进行比较,试验仿真结果证实了该算法的有效性.

In practice, the noise statistics are usually unknown or not perfectly known. To deal with the estimation problem in linear discrete-time systems with Markov jump parameters, where the measurement noise covariance is unknown, a approach was presented. This approach was based on the interacting multiple model （IMM） framework. A H∞ filter was employed to construct a noise statistics estimator to obtain the information which was necessary for the IMM algorithm. In the proposed approach, the noise statistics loss problem was solved while the merits of IMM algorithm was reserved. The effectiveness of the proposed approach was demonstrated in comparison with single-model H∞, filter through Monte Carlo simulation for maneuvering target tracking.