具有测量原点不确定度的跟踪滤波器的Lyapunov稳定性

SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603) Pub Date : 2001-07-25 DOI:10.1109/SICE.2001.977851

Yong-Shik Kim, K. Hong

{"title":"具有测量原点不确定度的跟踪滤波器的Lyapunov稳定性","authors":"Yong-Shik Kim, K. Hong","doi":"10.1109/SICE.2001.977851","DOIUrl":null,"url":null,"abstract":"The probabilistic data association filter (PDAF) is known to provide better tracking performance than the standard Kalman filter (KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al. (1985), in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to evaluate the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom (1991), is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.","PeriodicalId":415046,"journal":{"name":"SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lyapunov stability of a tracking filter with the uncertainty of measurement origin\",\"authors\":\"Yong-Shik Kim, K. Hong\",\"doi\":\"10.1109/SICE.2001.977851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The probabilistic data association filter (PDAF) is known to provide better tracking performance than the standard Kalman filter (KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al. (1985), in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to evaluate the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom (1991), is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.\",\"PeriodicalId\":415046,\"journal\":{\"name\":\"SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SICE.2001.977851\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SICE.2001.977851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

已知概率数据关联滤波器(PDAF)在混乱环境中比标准卡尔曼滤波器(KF)提供更好的跟踪性能。本文研究了Fortmann et al.(1985)在测量原点存在不确定性的情况下PDAF的稳定性。用修正的Riccati方程逼近两个随机项及其期望，来评价PDAF的稳定性。与Li和Bar-Shalom(1991)的定量评价不同，本文提出了一种新的基于Lyapunov函数的方法。在假设系统和观测噪声有界的情况下，建立了具体的跟踪误差界。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Lyapunov stability of a tracking filter with the uncertainty of measurement origin

The probabilistic data association filter (PDAF) is known to provide better tracking performance than the standard Kalman filter (KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al. (1985), in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to evaluate the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom (1991), is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603)

自引率

0.00%

发文量