Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities
The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy iss...
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doaj-b5ee89ad409b4010b5edb084989924b12021-03-30T01:11:51ZengIEEEIEEE Access2169-35362020-01-0184515452110.1109/ACCESS.2019.29634458948003Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth IntensitiesFeihu Zhang0https://orcid.org/0000-0002-1774-727XLe Li1https://orcid.org/0000-0002-0412-7565Lichuan Zhang2https://orcid.org/0000-0001-8818-5721Guang Pan3https://orcid.org/0000-0003-1932-8252School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, ChinaThe key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity.https://ieeexplore.ieee.org/document/8948003/Cooperative localizationpoint matchingprobability hypothesis density (PHD) filter |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Feihu Zhang Le Li Lichuan Zhang Guang Pan |
spellingShingle |
Feihu Zhang Le Li Lichuan Zhang Guang Pan Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities IEEE Access Cooperative localization point matching probability hypothesis density (PHD) filter |
author_facet |
Feihu Zhang Le Li Lichuan Zhang Guang Pan |
author_sort |
Feihu Zhang |
title |
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities |
title_short |
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities |
title_full |
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities |
title_fullStr |
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities |
title_full_unstemmed |
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities |
title_sort |
multiple vessel cooperative localization under random finite set framework with unknown birth intensities |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity. |
topic |
Cooperative localization point matching probability hypothesis density (PHD) filter |
url |
https://ieeexplore.ieee.org/document/8948003/ |
work_keys_str_mv |
AT feihuzhang multiplevesselcooperativelocalizationunderrandomfinitesetframeworkwithunknownbirthintensities AT leli multiplevesselcooperativelocalizationunderrandomfinitesetframeworkwithunknownbirthintensities AT lichuanzhang multiplevesselcooperativelocalizationunderrandomfinitesetframeworkwithunknownbirthintensities AT guangpan multiplevesselcooperativelocalizationunderrandomfinitesetframeworkwithunknownbirthintensities |
_version_ |
1724187519088590848 |