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Maritime environments with their ever-changing conditions could be hostile to cargo, civilian, and military vessels. In recent years, the adversarial attacks have increased considerably and more marine areas are deemed unsafe than ever before. Detection and recognition of fast moving boats is of particular interest since they project more hostility and possess high degree of maneuverability. However, high-speed boats generate long-lasting ship-wakes. The ship-wakes that are due to unsteady hydrodynamics characteristics of ocean water, naturally formed behind the boats, and can reveal several important features about the boats as well as depicting their spatiotemporal behavioral activities. Through remote sensing applications and by scrutinizing the wake patterns via robust deep learning classifiers, both boats’ features and their hostile or normal behavioral activities can be dependently discriminated. This paper presents methods for simulation of different speedboat behaviors and their associated wake formations using Houdini’s FLIP hydrodynamic particle simulation toolbox. Using this technique, we created different boat activities and obtained their simulated ocean wake formations. From each model, we extracted and formed topographical maps corresponding to the wake formations and employed them as ocean wake layer in our remote sensing simulation software, called IRIS. IRIS simulates large-scale physics-based electromagnetic (EM) environments. Using IRIS-EM techniques, we modeled different physics-based marine boats along with exemplar wake formations. We systematically generated and annotated multi-look, multi-range simulated synthetic aperture radar imagery from our marine test models. In this paper, we compare our hydrodynamic particle modeling approach vs an image-based approach and present a comparison of results of both methods and discuss their trade-offs as candidates for the Synthetic SAR imagery generation and deep learning systems development.
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