[1] Xu Mai, Shan Xiuming, Xu Baoguo,. Maneuvering target trackingusing threshold interacting multiple model algorithm [J]. Journal of Southeast University (English Edition), 2005, 21 (4): 440-444. [doi:10.3969/j.issn.1003-7985.2005.04.013]

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Maneuvering target trackingusing threshold interacting multiple model algorithm()

机动目标跟踪的TIMM算法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

21

Issue:

2005 4

Page:

440-444

Research Field:

Electromagnetic Field and Microwave Technology

Publishing date:

2005-12-30

Info

Title:

Maneuvering target trackingusing threshold interacting multiple model algorithm

机动目标跟踪的TIMM算法

Author(s):

Xu Mai¹, Shan Xiuming¹, Xu Baoguo²

¹Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
²School of Communication and Control Engineering, Southern Yangtze University, Wuxi 214036, China

徐迈¹, 山秀明¹, 徐保国²

¹清华大学电子工程系, 北京 100084; ²江南大学通信与控制学院, 无锡 214036

Keywords:

maneuvering target tracking;  Kalman filter;  interacting multiple model(IMM);  threshold interacting multiple model(TIMM)

机动目标跟踪;  卡尔曼滤波器;  相互作用多模型估计器;  具有门限的相互作用多模型估计器

PACS:

TN953

DOI:

10.3969/j.issn.1003-7985.2005.04.013

Abstract:

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model(TIMM)is proposed.This algorithm is based on the interacting multiple model(IMM)method and applies a threshold controller to improve tracking accuracy.It is also applicable to other advanced algorithms of IMM.In this research, we also compare the position and velocity root mean square(RMS)errors of TIMM and IMM algorithms with two different examples.Simulation results show that the TIMM algorithm is superior to the traditional IMM algorithm in estimation accuracy.

针对如何避免或减少由于目标的机动运动所带来的估计误差问题, 提出了具有门限的相互作用多模型估计(TIMM)的新算法.该算法主要应用于雷达目标自动跟踪系统中.TIMM算法在相互作用多模型估计器(IMM)算法的基础上引入门限控制器来提高跟踪精度, 该算法同样适用于其他各种改进的IMM算法.通过2个不同的例子, 对由TIMM和IMM这2种算法产生的均方根误差进行比较.仿真结果表明, 同IMM算法相比较, TIMM算法可减少估计误差, 从而提高机动目标的跟踪性能.

References:

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[9] Atherton D P, Lin H J.Parallel implementation of IMM tracking algorithm using transputers [A].In:IEE Proceeding:Radar, Sonar and Navigation[C].Brest, French, 1994.325-332.
[10] Johnston L A, Krishnamurthy V.An improvement to the interacting multiple model(IMM)algorithm [J].IEEE Transactions on Signal Processing, 2001, 49(12):2909-2923.
[11] Mazor E, Averbuch A Y, Bar-Shalom, et al.Interacting multiple model methods in target tracking:a survey [J].IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1):103-123.
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Memo

Memo:

Biographies: Xu Mai(1981—), male, graduate, xumai03@mails.tsinghua.edu.cn;Shan Xiuming(corresponding author), male, professor, shanxm@tsinghua.edu.cn.

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