Interacting Multiple Region Model

QU Yan-wen; ZHANG Er-hua; YANG Jing-yu

doi:10.3969/j.issn.0372-2112.2012.06.028

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Interacting Multiple Region Model

更新时间：2025-07-16

- Interacting Multiple Region Model
- Acta Electronica Sinica Vol. 40, Issue 6, Pages: 1235-1239(2012)
- 作者机构：
  
  1. 江西师范大学计算机信息工程学院,江西,南昌,330000
  2. 南京理工大学计算机科学与技术学院,江苏,南京,210094
  3. 江西师范大学计算机信息工程学院江西南昌,330000
  4. 南京理工大学计算机科学与技术学院江苏南京,210094
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.90820306)
- DOI：10.3969/j.issn.0372-2112.2012.06.028
  CLC： TP301.6
- Published：2012
- 稿件说明：
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QU Yan-wen, ZHANG Er-hua, YANG Jing-yu. Interacting Multiple Region Model[J]. Acta Electronica Sinica, 2012, 40(6): 1235-1239.
DOI：

QU Yan-wen, ZHANG Er-hua, YANG Jing-yu. Interacting Multiple Region Model[J]. Acta Electronica Sinica, 2012, 40(6): 1235-1239. DOI： 10.3969/j.issn.0372-2112.2012.06.028.

摘要

一种被称为交互式多区域模型(IMRM)的非线性滤波算法被提出

用于对状态和连续系统参数进行联合估计.IMRM将连续的系统参数空间视为由若干子区域所构成的集合

并将每个子区域分别分配给一个子模型.IMRM使用一组子滤波器并行滤波.在每一时刻

IMRM利用交互操作计算各子模型的混合初始化环境

之后各子滤波器在假设系统参数跳变到特定子区域的前提下

对状态和系统参数进行估计.为了有效地应用IMRM

提出了一种基于无迹变换的交互式多区域模型(UT-IMRM)算法.UT-IMRM对每个子模型使用无迹卡尔曼滤波器(UKF)进行滤波.在目标跟踪实验中对UT-IMRM性能进行测试.实验结果显示当系统参数不属于IMM模型集合时

UT-IMRM能够比IMM获得更好的估计性能.

Abstract

A nonlinear filtering method called Interacting Multiple Region Model (IMRM) is proposed to estimate the state and continuous system parameter together.IMRM regards the continuous system parameter space as a set of disjoint sub-regions

and each sub-region is assigned to a sub-model respectively.IMRM runs a bank of sub-filters in parallel.At each time step

IMRM computes the mixed initial condition for each sub-model by interaction operation

and each sub-filter estimates the state and system parameter on the condition that the system parameter belongs to a unique sub-region.In order to implement the IMRM efficiently

Unscented Transformation based IMRM (UT-IMRM) is developed by using the unscented kalman filter as the sub-filter.A target tracking experiment is designed to test the performance of UT-IMRM.Experimental results show that UT-IMRM achieves better estimation performance than that of IMM when the system parameter doesnt belong to the IMM model set.

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