POSEIDON: An Analytical End-to-End Performance Prediction Model for Submerged Object Detection and Recognition by Lidar Fluorosensors in the Marine Environment

Abstract

An 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, POSEIDON embeds 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.