Mr. Dmitry A. Shepeta Profile

Dmitry A. Shepeta

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SPIE Journal Paper | 5 January 2022

Modeling of input signals reflected from coastal zones and observed by small-sized radar systems from UAVs

Vadim Nenashev, Dmitry Shepeta, Viktor Isakov

JARS, Vol. 16, Issue 01, 012015, (January 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.012015

KEYWORDS: Radar, Mathematical modeling, Coastal modeling, Reflection, Systems modeling, Correlation function, Algorithm development, Unmanned aerial vehicles, Statistical modeling, Data modeling

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When developing modern unmanned aerial vehicle-based onboard small-sized radar systems, which are used for monitoring of coastal zones for their operational and environmental purposes, adequate mathematical models are needed of reflections from the corresponding resolution elements of the underlying surfaces, located on the water–land interface. We consider the development of some mathematical models for such applications. We analyze two modern approaches used in developing mathematical models for input signals reflected both separately from the terrestrial and marine surfaces and simultaneously from two aforementioned surfaces at once. A statistical approach and two families of distributions are chosen for modeling the signals, which enable synthesis of efficient algorithms for modeling the fluxes of random variables. The input signals of sea surface reflections are approximated by the log-normal law, while the reflections from the land surface are approximated by the Weibull distribution. For edge-coastal modeling, it is proposed to use the law of distribution of the vector sum of the flux of random variables comprising the input signals reflected from elementary land and sea areas that simultaneously fall within the corresponding resolution element. A highlight of the models developed is the consideration of both the probability distribution laws of the reflected signals and their correlation-spectral characteristics between resolution elements, as well as the anisotropy of reflections when monitoring surfaces from several angles. The presented mathematical models are consistent with experimental data sets of reflections, obtained from coastal zones. The development of coastal reflection models and corresponding modeling algorithms reduces the required number of actual field tests and saves time for implementation of monitoring systems, and hence, limits the overhead of this implementation.

Proceedings Article | 14 November 2021 Presentation + Paper

Determination of the coastal edge using on-board radar

Gysberth Maurits Wattimena, Dmitry Shepeta, Victor Isakov

Proceedings Volume 11914, 119140D (2021) https://doi.org/10.1117/12.2603944

KEYWORDS: Smoothing, Radar, Reflection, Signal processing, Natural disasters, Modeling, Reflectivity, Natural surfaces, Metals, Digital filtering

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The definition of the coastal edge that defines the outline of the coastline, is one of the main tasks in the drafting of digital maps. The solution to this problem is extremely important for maritime navigation. It is important to constantly monitor the outlines of coastlines in connection with natural disasters that can change the coastal edge, such as landslides, tsunami, ice melting, etc. This, in turn, can lead to dangerous consequences for vessels carrying out civil transportation and humanitarian assistance in waters affected by natural disasters. The solution to this problem is of particular importance for archipelago countries such as Indonesia, England, etc The most common methods for determining the coastline are radar-optical methods using opticalradar systems located on-board unmanned small aerial vehicles flying according to predetermined programs. This makes it possible to determine the outlines of the coastline in an automatic (automated) mode, in fact, not depending on weather conditions, and to receive operational information in realtime, practically without time delays The paper considers and analyzes the algorithm for determining the coastal edge using smallsized radar stations located on small unmanned aerial vehicles. This allows you to fully automate the monitoring process. Depending on the characteristics of the on-board equipment and its operating modes, the accuracy of determining the land-sea edge can reach one and a half meters.

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