[1]张璐,张国良,张维平,等.改进IMM算法在机器人目标跟踪中的应用[J].计算机技术与发展,2013,(02):149-152.
　ZHANG Lu,ZHANG Guo-liang,ZHANG Wei-ping,et al.Application of Improved IMM Algorithm in Robot Target Tracking[J].,2013,(02):149-152.
点击复制

改进IMM算法在机器人目标跟踪中的应用()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年02期
页码:
149-152
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Application of Improved IMM Algorithm in Robot Target Tracking
文章编号:
1673-629X（2013）02-0149-04
作者:
张璐; 张国良;  张维平; 敬斌
第二炮兵工程大学
Author(s):
ZHANG Lu; ZHANG Guo-liang; ZHANG Wei-ping; JING Bin
关键词:
目标跟踪; "当前"统计模型; 交互式多模型算法
Keywords:
target tracking; "current" statistical model; Interactive Multi-Model algorithm
文献标志码:
A
摘要:
在机器人对运动目标跟踪的过程中,由于目标运动状态具有多样化的特点,不能运用单一的运动模型对其进行跟踪.文中将当前统计模型(CS)和匀速模型(CV)交互,并自适应调节“当前”统计模型中的目标加速度和模型之间的转移概率,形成新的CVCSIMM算法,使其能够更有效地反映目标的机动特性.在Matlab上对本算法进行了仿真研究,并与基本CVCAIMM算法、改进前的CVCSIMM算法进行了比较. Monte Carlo仿真结果表明:本算法减小了跟踪过程中的误差,提高了对机动目标的跟踪精度
Abstract:
In the tracking process of robot for moving target,since the target motion state has diversified characteristics,can not use a sin-gle movement model to carry on the track. In this paper,the current statistical model (CS) and uniform model (CV) is interacted,and the target's acceleration of the"current" statistical model and the transition probability between the models are adjusted adaptively to form a new CVCSIMM algorithm, so that it fully reflects the maneuvering characteristics of the target. The Monte Carlo simulation is carried out using Matlab software,which is compared with basic CVCAIMM algorithm and the old CVCSIMM algorithm. The results show that the proposed algorithm reduces the error in the tracking process and improves the tracking precision of maneuvering target

相似文献/References:

[1]刘翔 吴谨 祝愿博 康晓晶.基于视频序列的目标检测与跟踪技术研究[J].计算机技术与发展,2009,(11):179.
　LIU Xiang,WU Jin,ZHU Yuan-bo,et al.A Study of Object Detecting and Tracking Based on Video Sequences[J].,2009,(02):179.
[2]雷云 王夏黎 孙华.基于视频的交通目标跟踪方法研究[J].计算机技术与发展,2010,(07):44.
　LEI Yun,WANG Xia-li,SUN Hua.The Research about Transport Target Tracking Based on Video[J].,2010,(02):44.
[3]谢之宇 蒋晓瑜 汪熙 裴闯.基于多线索融合的目标跟踪算法研究[J].计算机技术与发展,2011,(03):125.
　XIE Zhi-yu,JIANG Xiao-yu,WANG Xi,et al.A Target Tracking Algorithm Research Based on Multi-Cue Fusion[J].,2011,(02):125.
[4]尤天来 周海徽[].红外目标跟踪技术研究[J].计算机技术与发展,2011,(10):140.
　YOU Tian-lai,ZHOU Hai-hui.Research of Infrared Target Tracking Technology[J].,2011,(02):140.
[5]赵侃 漆德宁.基于UKF滤波的FDOA和TDOA联合定位跟踪算法[J].计算机技术与发展,2012,(05):127.
　ZHAO Kan,QI De-ning.A Tracking TDOA/FDOA Joint Location Algorithm Based on UKF[J].,2012,(02):127.
[6]姚放吾 许辰铭.基于目标质心的Meanshift跟踪算法[J].计算机技术与发展,2012,(06):104.
　YAO Fang-wu,XU Chen-ming.A Meanshift Tracking Algorithm Based on Centroid[J].,2012,(02):104.
[7]吴佳家,高珏,李敏,等.基于均值漂移和卡尔曼滤波的跟踪算法研究[J].计算机技术与发展,2014,24(01):5.
　WU Jia-jia[,GAO Jue[],LI Min[],et al.Research on Target Tracking Algorithm Based on Mean Shift and Kalman Filter[J].,2014,24(02):5.
[8]郝杰,任静. 高超声速飞行器交互式多模型跟踪算法仿真[J].计算机技术与发展,2015,25(02):204.
　HAO Jie,REN Jing. Tracking Algorithm Simulation of Interactive Multiple Model for Hypersonic Flight Vehicle[J].,2015,25(02):204.
[9]高翔,朱婷婷,刘洋. 多摄像头系统的目标检测与跟踪方法研究[J].计算机技术与发展,2015,25(07):221.
　GAO Xiang,ZHU Ting-ting,LIU Yang. Research of Target Detection and Tracking Method for Multi-camera System[J].,2015,25(02):221.
[10]刘少林,朱文球,孙文静,等.基于联合直方图的自适应粒子滤波跟踪算法[J].计算机技术与发展,2018,28(06):106.[doi:10.3969/ j. issn.1673-629X.2018.06.024]
　LIU Shao-lin,ZHU Wen-qiu,SUN Wen-jing,et al.An Adaptive Particle Filtering and Tracking Algorithm Based on Joint Histogram[J].,2018,28(02):106.[doi:10.3969/ j. issn.1673-629X.2018.06.024]

更新日期/Last Update: 1900-01-01