Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloPOSEIDON: An Analytical End-to-End Performance Prediction Model for Submerged Object Detection and Recognition by Lidar Fluorosensors in the Marine Environment
Anno di pubblicazione2017
Autore/iMatteoli S.; Zotta L.; Diani M.; Corsini G.
Affiliazioni autoriNational Research Council (CNR) of Italy, Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Pisa 56122, Italy (e-mail:, ; Department of Aerospace and Defence, GMV Innovating Solutions, Harwell Campus, Didcot OX11 0QS, United Kingdom of Great Britain and Northern Ireland (e-mail:, ; Dipartimento Armi Navali, Accademia Navale, Livorno 57127, Italy (e-mail:, ; Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa 56122, Italy (e-mail:,
Autori CNR e affiliazioni
  • inglese
AbstractAn analytical end-to-end model is developed to predict the performance of underwater object recognition by means of light detection and ranging (lidar) fluorosensors, as an aid to underwater lidar mission planning and system design. The proposed Performance prediction mOdel for Submerged object dEtection and recognitIon by liDar fluOrosensors in the marine eNvironment (POSEIDON) reproduces the overall end-to-end fluorescence lidar system chain-from signal generation, to signal propagation, acquisition, and processing. The goal is assessing the performance that may be obtained for spectral recognition of an underwater object in various operational scenarios in terms of several different performance metrics. In addition to the performance prediction models developed in the literature for airborne lidar bathymetry, POSEIDONembeds a novel comprehensive signal simulator that accounts for inelastic scattering phenomena as well as a signal processing module designed ad hoc to accomplish spectral recognition of an underwater object with respect to a data base of objects of interest spectrally characterized by their fluorescence spectral signatures. Test cases with a lidar system arranged in two configurations and several objects submerged at various depths in different Cases I and II waters were reproduced and explored. Results obtained within a Monte Carlo simulation framework provide proof-of-concept of POSEIDON performance forecasting capabilities for underwater object recognition.
Lingua abstractinglese
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RivistaIEEE journal of selected topics in applied earth observations and remote sensing (Print)
Attiva dal 2008
Editore: IEEE, - Piscataway, N.J.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1939-1404
Titolo chiave: IEEE journal of selected topics in applied earth observations and remote sensing (Print)
Titolo proprio: IEEE journal of selected topics in applied earth observations and remote sensing. (Print)
Titolo abbreviato: IEEE j. sel. top. appl. earth obs. remote sens. (Print)
Titoli alternativi:
  • Institute of Electrical and Electronic Engineers journal of selected topics in applied earth observations and remote sensing (Print)
  • Journal of selected topics in applied earth observations and remote sensing (Print)
  • J-STARS (Print)
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Verificato da refereeSì: Internazionale
Stato della pubblicazionePostprint
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85028729995)
Parole chiaveunderwater target detection, fluorescence lidar, underwater signal propagation, spectral signature, object recognition
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Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR
    Progetti Europei-