Strong-Tracking Multiple Model Estimator

LIANG Yan; PAN Quan; JIA Yu-gang; ZHOU Dong-hua

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Strong-Tracking Multiple Model Estimator

更新时间：2025-07-16

- Strong-Tracking Multiple Model Estimator
- Acta Electronica Sinica Vol. 30, Issue 1, Pages: 34-37(2002)
- 作者机构：
  
  1. 清华大学自动化系,北京,100084
  2. 西北工业大学自动控制系,陕西,西安,710072
  3. 清华大学自动化系北京,100084
  4. 西北工业大学自动控制系陕西西安,710072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN713
- Published：2002
- 稿件说明：
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LIANG Yan, PAN Quan, JIA Yu-gang, et al. Strong-Tracking Multiple Model Estimator[J]. Acta Electronica Sinica, 2002, 30(1): 34-37.
DOI：

LIANG Yan, PAN Quan, JIA Yu-gang, et al. Strong-Tracking Multiple Model Estimator[J]. Acta Electronica Sinica, 2002, 30(1): 34-37. DOI：

摘要

本文提出了一种基于最小二乘估计的强跟踪滤波器(STF)单重渐消因子求解方法.从参数自适应与模型自适应有机结合的角度出发

将STF与交互式多模型算法(IMM)相结合

设计了强跟踪交互式多模型估计器(STMME).仿真表明:STMME在跟踪机动目标时

对速度

加速度的跟踪精度明显优于传统的IMM

在自适应估计领域有着较好的应用前景.

Abstract

Firstly we analyse the properties of Strong Tracking Filter (STF) and Interacting Multiple Model Algorithm and find that STF is a parameter-adaptive algorithm and IMM is a model-adaptive algorithm.It means that they may be combined effectively.Secondly we propose a new method based on the Least-Squared Estimation to search for the fading factor in STF.After that

we design a Strong-Tracking Multiple Model Estimator (STMME) by combining the new STF with IMM.Finally

the simulations show that STMME greatly improves accuracy of velocity and acceleration compared with the conditional IMM when tracking the maneuvering target.And the computation burden increases only 6%.

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