dc.contributor.author | Munafò, Andrea | |
dc.contributor.author | Ferri, Gabriele | |
dc.contributor.author | LePage, Kevin D. | |
dc.contributor.author | Goldhahn, Ryan A. | |
dc.date.accessioned | 2019-06-19T09:40:38Z | |
dc.date.available | 2019-06-19T09:40:38Z | |
dc.date.issued | 2019/06 | |
dc.identifier.govdoc | CMRE-PR-2019-062 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/802 | |
dc.description.abstract | Autonomous Underwater Vehicles (AUVs) present a low-cost alternative or supplement to existing underwater surveillance networks. The NATO STO Centre for Maritime Research and Experimentation is developing collaborative autonomous behaviours to improve the performance of multi-static networks of AUVs. In this work we lay the foundation to combine a range-dependent acoustic model with a three dimensional measurement model for a linear array within a Bayesian framework. The resulting algorithm is able to provide the vehicles with an estimation of the target depth together with the more usual information based on a planar assumption (i.e. target latitude and longitude). Results are shown through simulations and as obtained from the REP16 sea trial where for the first time a preliminary implementation of the method was deployed in the C-OEX vehicles. | en_US |
dc.format | 8 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: OCEANS 2017 - Aberdeen, UK, 19-22 June 2017, doi: 10.1109/OCEANSE.2017.8084874 | en_US |
dc.subject | Autonomous Underwater Vehicles (AUV) | en_US |
dc.subject | Underwater surveillance | en_US |
dc.subject | Sensor networks | en_US |
dc.subject | Acoustic models | en_US |
dc.subject | Bistatic sonar | en_US |
dc.subject | Sonar arrays | en_US |
dc.subject | Target localization | en_US |
dc.subject | Target depth | en_US |
dc.subject | Algorithms | en_US |
dc.subject | REP'16 Atlantic trial | en_US |
dc.title | AUV active perception: exploiting the water column | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |