An Algorithm of Sensor Management Based on RIMMKF

LIU Xian-xing; ZHOU Lin; DU Xiao-yu

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An Algorithm of Sensor Management Based on RIMMKF

更新时间：2025-07-16

- An Algorithm of Sensor Management Based on RIMMKF
- Acta Electronica Sinica Vol. 35, Issue 12, Pages: 2247-2251(2007)
- 作者机构：
  
  1. 河南大学先进控制与智能信息研究所,河南,开封,475001
  2. 河南大学计算机与信息工程学院,河南,开封,475001
  3. 河南大学基础实验教学中心,河南,开封,475001
  4. 河南大学先进控制与智能信息研究所河南开封,475001
  5. 河南大学计算机与信息工程学院河南开封,475001
  6. 河南大学基础实验教学中心河南开封,475001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2007
- 稿件说明：
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LIU Xian-xing, ZHOU Lin, DU Xiao-yu. An Algorithm of Sensor Management Based on RIMMKF[J]. Acta Electronica Sinica, 2007, 35(12): 2247-2251.
DOI：

LIU Xian-xing, ZHOU Lin, DU Xiao-yu. An Algorithm of Sensor Management Based on RIMMKF[J]. Acta Electronica Sinica, 2007, 35(12): 2247-2251. DOI：

摘要

以重加权交互式多模型卡尔曼滤波(RIMMKF)为基础

提出了一种传感器管理算法.首先利用RIMM对联合多目标概率的量测更新和马尔可夫转移更新求取分辨力也即信息增量

然后利用信息增量最大化来分配传感器资源.所采用的模型弥补了交互式多模型(IMM)的不足.仿真结果表明

在机动多模型环境下

与IMMKF分辨力方法相比

本文所提算法行之有效且能使传感器资源得到有效地分配.

Abstract

This paper presents an algorithm of sensor management based on reweighted interacting multiple models kalman filtering(RIMMKF).In this algorithm

discrimination via

information gain is obtained by measurement update and Markov transform update of joint multi-target probability using RIMM

and then the sensor resource is distributed by maximizing information gain.In this algorithm

the deficiency of interacting multiple models (IMM) is improved by RIMM.Simulation results show that compared with the method of IMMKF discrimination

this algorithm is reasonable and the sensor resource is effectively distributed under the circumstance of dynamic multiple models.

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Related Institution

Dept.of ComputerHenan UniversityKaifeng 475001China

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Dept.of Computer,Henan University

Dept.of Automatic Control,Northwestern Ploytechnical University

Department of Automatic ControlNorthwestern Ploytechnical UniversityXi'anShaanxi 710072China

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