A semi-Markov multiple event filter for maneuvering targets

P. Abeles, M. Kovacich
{"title":"A semi-Markov multiple event filter for maneuvering targets","authors":"P. Abeles, M. Kovacich","doi":"10.1109/ICIF.2007.4407972","DOIUrl":null,"url":null,"abstract":"Tracking maneuvering targets is a difficult problem due to unpredictable maneuvers which change the target's state and/or dynamics. To ensure track accuracy a filter needs to model the target correctly and quickly respond to maneuvers. A new sequential filter is proposed which attempts to improve upon existing algorithms in several areas. A more flexible internal model is used to describe effects of maneuver events. Maneuver hypotheses have improved temporal accuracy. A semi-Markov process is used to describe the probability of an event occurring as a function of time. In simulated test scenarios the new algorithm performs as well as or significantly better than Interacting Multiple Model filter.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 10th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2007.4407972","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Tracking maneuvering targets is a difficult problem due to unpredictable maneuvers which change the target's state and/or dynamics. To ensure track accuracy a filter needs to model the target correctly and quickly respond to maneuvers. A new sequential filter is proposed which attempts to improve upon existing algorithms in several areas. A more flexible internal model is used to describe effects of maneuver events. Maneuver hypotheses have improved temporal accuracy. A semi-Markov process is used to describe the probability of an event occurring as a function of time. In simulated test scenarios the new algorithm performs as well as or significantly better than Interacting Multiple Model filter.
机动目标的半马尔可夫多事件滤波器
机动目标的跟踪是一个难以预测的问题,因为机动会改变目标的状态和/或动力学。为了确保跟踪精度,滤波器需要正确地对目标建模并快速响应机动。提出了一种新的顺序滤波器,它试图在几个方面改进现有的算法。采用更灵活的内部模型来描述机动事件的影响。机动假设提高了时间精度。半马尔可夫过程用于描述事件发生的概率作为时间的函数。在模拟测试场景中,新算法的性能与交互多模型滤波器相当或明显优于多模型滤波器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信