The Robust Passive Location Algorithm for Maneuvering Target Tracking

With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used...

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Main Authors: Xiaojun Yang, Gang Liu, Jinku Guo, Hongqiao Wang, Bing He
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/404986
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spelling doaj-8d0f41a11b6e48d895ec0d51eef847c82020-11-24T23:14:53ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/404986404986The Robust Passive Location Algorithm for Maneuvering Target TrackingXiaojun Yang0Gang Liu1Jinku Guo2Hongqiao Wang3Bing He4Xi’an Research Institute of High Technology, Xi’an, Shaanxi 710025, ChinaXi’an Research Institute of High Technology, Xi’an, Shaanxi 710025, ChinaXi’an Research Institute of High Technology, Xi’an, Shaanxi 710025, ChinaXi’an Research Institute of High Technology, Xi’an, Shaanxi 710025, ChinaXi’an Research Institute of High Technology, Xi’an, Shaanxi 710025, ChinaWith the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.http://dx.doi.org/10.1155/2015/404986
collection DOAJ
language English
format Article
sources DOAJ
author Xiaojun Yang
Gang Liu
Jinku Guo
Hongqiao Wang
Bing He
spellingShingle Xiaojun Yang
Gang Liu
Jinku Guo
Hongqiao Wang
Bing He
The Robust Passive Location Algorithm for Maneuvering Target Tracking
Mathematical Problems in Engineering
author_facet Xiaojun Yang
Gang Liu
Jinku Guo
Hongqiao Wang
Bing He
author_sort Xiaojun Yang
title The Robust Passive Location Algorithm for Maneuvering Target Tracking
title_short The Robust Passive Location Algorithm for Maneuvering Target Tracking
title_full The Robust Passive Location Algorithm for Maneuvering Target Tracking
title_fullStr The Robust Passive Location Algorithm for Maneuvering Target Tracking
title_full_unstemmed The Robust Passive Location Algorithm for Maneuvering Target Tracking
title_sort robust passive location algorithm for maneuvering target tracking
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.
url http://dx.doi.org/10.1155/2015/404986
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