Estimation filtering for Deep Water Navigation
The navigation task for Unmanned Underwater Vehicles is made difficult in a deep water scenario because of the lack of bottom lock for Doppler Velocity Log (DVL). This is due to the operating altitude that, for this kind of applications, is typically greater than the sensor maximum range. The effect...
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ftnerc:oai:nora.nerc.ac.uk:521495 2023-05-15T16:51:09+02:00 Estimation filtering for Deep Water Navigation Costanzi, Riccardo Fenucci, Davide Caiti, Andrea Micheli, Michele Vermeij, Arjan Tesei, Alessandra Munafo, Andrea 2018 text http://nora.nerc.ac.uk/id/eprint/521495/ https://nora.nerc.ac.uk/id/eprint/521495/1/1-s2.0-S2405896318322080-main.pdf https://doi.org/10.1016/j.ifacol.2018.09.519 en eng https://nora.nerc.ac.uk/id/eprint/521495/1/1-s2.0-S2405896318322080-main.pdf Costanzi, Riccardo; Fenucci, Davide; Caiti, Andrea; Micheli, Michele; Vermeij, Arjan; Tesei, Alessandra; Munafo, Andrea. 2018 Estimation filtering for Deep Water Navigation. IFAC-PapersOnLine, 51 (29). 299-304. https://doi.org/10.1016/j.ifacol.2018.09.519 <https://doi.org/10.1016/j.ifacol.2018.09.519> Publication - Article PeerReviewed 2018 ftnerc https://doi.org/10.1016/j.ifacol.2018.09.519 2023-02-04T19:47:20Z The navigation task for Unmanned Underwater Vehicles is made difficult in a deep water scenario because of the lack of bottom lock for Doppler Velocity Log (DVL). This is due to the operating altitude that, for this kind of applications, is typically greater than the sensor maximum range. The effect is that the velocity measurements are biased by sea currents resulting in a rapidly increasing estimation error drift. The solution proposed in this work is based on a distributed, cooperative strategy strongly relying on an acoustic underwater network. According to the distributed philosophy, an instance of a specifically designed navigation filter (named DWNF - Deep Water Navigation Filter) is executed by each vehicle. Each DWNF relies on different Extended Kalman Filters (EKFs) running in parallel on-board: one for own navigation state estimation (AUV-EKF), the other ones for the navigation state of the remaining assets (Asset-EKF). The AUV-EKF is designed to simultaneously estimate the vehicle position and the sea current for more reliable predictions. The DWNF builds in real-time a database of past measurements and estimations; in this way it can correctly deal with delayed information. An outlier detection and rejection policy based on the Mahalanobis distance associated to each measurement is implemented. The experimental validation of the proposed approach took place in a deep water scenario during the Dynamic Mongoose’17 exercise off the South coast of Iceland (June-July 2017); preliminary analysis of the results is presented. Article in Journal/Newspaper Iceland Natural Environment Research Council: NERC Open Research Archive IFAC-PapersOnLine 51 29 299 304 |
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Natural Environment Research Council: NERC Open Research Archive |
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English |
description |
The navigation task for Unmanned Underwater Vehicles is made difficult in a deep water scenario because of the lack of bottom lock for Doppler Velocity Log (DVL). This is due to the operating altitude that, for this kind of applications, is typically greater than the sensor maximum range. The effect is that the velocity measurements are biased by sea currents resulting in a rapidly increasing estimation error drift. The solution proposed in this work is based on a distributed, cooperative strategy strongly relying on an acoustic underwater network. According to the distributed philosophy, an instance of a specifically designed navigation filter (named DWNF - Deep Water Navigation Filter) is executed by each vehicle. Each DWNF relies on different Extended Kalman Filters (EKFs) running in parallel on-board: one for own navigation state estimation (AUV-EKF), the other ones for the navigation state of the remaining assets (Asset-EKF). The AUV-EKF is designed to simultaneously estimate the vehicle position and the sea current for more reliable predictions. The DWNF builds in real-time a database of past measurements and estimations; in this way it can correctly deal with delayed information. An outlier detection and rejection policy based on the Mahalanobis distance associated to each measurement is implemented. The experimental validation of the proposed approach took place in a deep water scenario during the Dynamic Mongoose’17 exercise off the South coast of Iceland (June-July 2017); preliminary analysis of the results is presented. |
format |
Article in Journal/Newspaper |
author |
Costanzi, Riccardo Fenucci, Davide Caiti, Andrea Micheli, Michele Vermeij, Arjan Tesei, Alessandra Munafo, Andrea |
spellingShingle |
Costanzi, Riccardo Fenucci, Davide Caiti, Andrea Micheli, Michele Vermeij, Arjan Tesei, Alessandra Munafo, Andrea Estimation filtering for Deep Water Navigation |
author_facet |
Costanzi, Riccardo Fenucci, Davide Caiti, Andrea Micheli, Michele Vermeij, Arjan Tesei, Alessandra Munafo, Andrea |
author_sort |
Costanzi, Riccardo |
title |
Estimation filtering for Deep Water Navigation |
title_short |
Estimation filtering for Deep Water Navigation |
title_full |
Estimation filtering for Deep Water Navigation |
title_fullStr |
Estimation filtering for Deep Water Navigation |
title_full_unstemmed |
Estimation filtering for Deep Water Navigation |
title_sort |
estimation filtering for deep water navigation |
publishDate |
2018 |
url |
http://nora.nerc.ac.uk/id/eprint/521495/ https://nora.nerc.ac.uk/id/eprint/521495/1/1-s2.0-S2405896318322080-main.pdf https://doi.org/10.1016/j.ifacol.2018.09.519 |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
https://nora.nerc.ac.uk/id/eprint/521495/1/1-s2.0-S2405896318322080-main.pdf Costanzi, Riccardo; Fenucci, Davide; Caiti, Andrea; Micheli, Michele; Vermeij, Arjan; Tesei, Alessandra; Munafo, Andrea. 2018 Estimation filtering for Deep Water Navigation. IFAC-PapersOnLine, 51 (29). 299-304. https://doi.org/10.1016/j.ifacol.2018.09.519 <https://doi.org/10.1016/j.ifacol.2018.09.519> |
op_doi |
https://doi.org/10.1016/j.ifacol.2018.09.519 |
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IFAC-PapersOnLine |
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51 |
container_issue |
29 |
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299 |
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