一种多模型滤波的分级递阶模型集调整方法

A Hierarchical Model-Set Adaptation Method for Multiple Model Filter

模型集的动态调整方案是变结构多模型滤波的核心问题之一.文中借鉴生物免疫系统中B细胞识别抗原的工作原理,提出一种分级递阶式的模型集调整方法.它将系统模型集分解成基础网格和移动网格两个部分,由基础网格探测系统模式的大致分布区域,由移动网格在基础网格指示区域进行精细搜索获得滤波结果.由于该方法将普通较大的系统模型集分解成由两个较小模型集构成的递阶结构,有效减小交互式滤波算法的计算时间.仿真实验表明,该方法利用较少的滤波时间能够获得较高的滤波精度.

Model-set adaptation method is important for VSMM. In this paper, a hierarchical model-set adaptation method is proposed, which mimics the working principle for B cells recognizing antigens in immune system. According to this method, the system model-set is decomposed into base grid and moving grid. The base grid is used to detect the distribution of system model by rough searching and the moving grid is used to obtain the final filtering results by fine searching the area indicated by base grid. The calculation time of interactive filtering algorithm is effectively reduced, since the whole large model-set is decomposed into two small hierarchical model-sets. The simulation results show that higher filtering accuracy can be obtained with less calculation time.