Cargando…
Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV
This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was esta...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535135/ https://www.ncbi.nlm.nih.gov/pubmed/37765922 http://dx.doi.org/10.3390/s23187865 |
_version_ | 1785112558333591552 |
---|---|
author | Chen, Tao Qi, Qi |
author_facet | Chen, Tao Qi, Qi |
author_sort | Chen, Tao |
collection | PubMed |
description | This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle’s (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking. |
format | Online Article Text |
id | pubmed-10535135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105351352023-09-29 Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV Chen, Tao Qi, Qi Sensors (Basel) Article This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle’s (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking. MDPI 2023-09-13 /pmc/articles/PMC10535135/ /pubmed/37765922 http://dx.doi.org/10.3390/s23187865 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Tao Qi, Qi Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title | Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title_full | Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title_fullStr | Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title_full_unstemmed | Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title_short | Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV |
title_sort | research on the cooperative target state estimation and tracking optimization method of multi-uuv |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535135/ https://www.ncbi.nlm.nih.gov/pubmed/37765922 http://dx.doi.org/10.3390/s23187865 |
work_keys_str_mv | AT chentao researchonthecooperativetargetstateestimationandtrackingoptimizationmethodofmultiuuv AT qiqi researchonthecooperativetargetstateestimationandtrackingoptimizationmethodofmultiuuv |