自适应转移概率交互式多模型跟踪算法

doi:10.3969/j.issn.0372-2112.2017.09.009

电子学报 ›› 2017, Vol. 45 ›› Issue (9): 2113-2120.DOI: 10.3969/j.issn.0372-2112.2017.09.009

自适应转移概率交互式多模型跟踪算法

许登荣, 程水英, 包守亮

国防科技大学电子对抗学院, 安徽合肥 230037

收稿日期:2016-04-19 修回日期:2016-10-03 出版日期:2017-09-25
- 作者简介:
- 许登荣,男,1991年4月出生于湖南邵阳.硕士,现为93046部队的助理工程师,主要研究方向为雷达数据处理.E-mail:xudengrong@126.com;程水英,男,1974年11月出生于安徽舒城,博士,现为电子工程学院副教授.主要主要研究方向有机动目标跟踪、非线性滤波、软件无线电、DSP及应用等.E-mail:csyebox@163.com;包守亮,男,1993年5月出生于四川达州.现为电子工程学院硕士研究生.主要研究方向为雷达数据处理.E-mail:15656036629@163.com

Interacting Multiple Model Algorithm Based on Adaptive Transition Probability

XU Deng-rong, CHENG Shui-ying, BAO Shou-liang

Institute of Electronic Countermeasure, National University of Defense Technology, Hefei, Anhui 230037, China

Received:2016-04-19 Revised:2016-10-03 Online:2017-09-25 Published:2017-09-25

摘要/Abstract

摘要： 针对标准的交互式多模型算法（Interacting Multiple Model，IMM）存在模型集设计困难和采用固定转移概率矩阵导致模型切换缓慢、跟踪精度下降的不足，提出一种自适应转移概率IMM算法.首先，提出了一种新的模型集设计方法，将强跟踪修正输入估计（Strong Tracking Modified Input Estimation，STMIE）模型和匀速运动（Constant Velocity，CV）模型作为IMM算法的模型集，利用STMIE算法对高机动目标的跟踪能力以及CV模型对非机动目标跟踪的高精度，实现对目标的全面自适应跟踪.其次，提出一种依据模型似然函数值对Markov转移概率进行实时修正的方法，增强匹配模型的作用，削弱不匹配模型的影响.仿真结果表明，依据模型似然函数修正转移概率的方法使IMM算法的模型切换速度和跟踪精度都得到提高，提出的IMM-STMIECV算法的跟踪精度高于IMM-CVCA、IMM-CVCACT以及IMM-CVCS算法.

关键词: 机动目标跟踪, 交互式多模型算法, Markov转移概率, 修正的输入估计法, 强跟踪

Abstract: There are two shortcomings in the standard interacting multiple model (IMM) algorithm:one is that designing models is difficult,the other is that the application of constant transition probability matrices makes the model switching speed slow and tracking accuracy decreased.To overcome these shortcomings,an IMM algorithm with adaptive transition probability is proposed.Firstly,a new model-set design method is proposed,and the strong tracking modified input estimation (STMIE) model and constant velocity (CV) model are adopted as the model sets of the IMM algorithm.By using the capability of STMIE model to track high maneuvering targets and the precision of CV model to track non-maneuvering targets,this algorithm can be comprehensively adaptive in target tracking.Secondly,a new method is proposed to modify the Markov transition probability in real time based on the likelihood values of the models,which enhances the effect of the matching model,and weakens the influence of the mismatched model.Simulation results show that the new method improves model switching speed and tracking precision of IMM algorithm,and the tracking precision of IMM-STMIECV algorithm is higher than that of IMM-CVCA,IMM-CVCACT and IMM-CVCS algorithms.

Key words: maneuvering target tracking, interacting multiple model (IMM), markov transition probability, modified input estimation (MIE), strong tracking filter (STF)

中图分类号:

TN95

许登荣, 程水英, 包守亮. 自适应转移概率交互式多模型跟踪算法[J]. 电子学报, 2017, 45(9): 2113-2120.

XU Deng-rong, CHENG Shui-ying, BAO Shou-liang. Interacting Multiple Model Algorithm Based on Adaptive Transition Probability[J]. Acta Electronica Sinica, 2017, 45(9): 2113-2120.

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自适应转移概率交互式多模型跟踪算法

Interacting Multiple Model Algorithm Based on Adaptive Transition Probability

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