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This paper presents a novel method of sea state characterization using the 'Mean Fractal Length (MFL)' criterion which is applied to experimental Synthetic Aperture Radar (SAR) one - dimensional signatures (range profiles), provided to our research group by SET 215 Working Group on 'SAR radar techniques'. The MFL criterion uses the 'blanket' technique to provide sea state characterization from SAR radar range profiles. It is based on the calculation of the area of a 'blanket', corresponding to the range profile under examination, and then on the calculation of the corresponding 'Fractal Length' of the range profile. The main idea concerning this proposed technique is the fact that SAR radar range profiles corresponding to different sea states yield different values of 'Fractal Length, FL', namely 'turbulent sea' yields range profiles with larger FL, because of the more 'anomalous behavior' of the range profiles in that case. As a result, a sea state characterization technique for two different sea states (turbulent and calm sea) is presented in this paper.

Original languageEnglish
Title of host publicationPROCEEDINGS OF 13 TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL APPLICATIONS (CEMA’18)
EditorsDimiter Dimitrov
Place of PublicationBulgaria, Sofia
Publisher TECHNICAL UNIVERSITY OF SOFIA
Pages1-5
Number of pages5
Volume2018-October
ISBN (Electronic)1304-2100
StatePublished - 18 Oct 2018
Event13 INTERNATIONAL CONFERENCE ON
COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL
APPLICATIONS (CEMA’18)
- Софийский технический университет, София, Bulgaria
Duration: 18 Oct 201820 Oct 2018
Conference number: 13
http://rcvt.tu-sofia.bg/CEMA/

Publication series

NameCommunication, Electromagnetics and Medical Application
ISSN (Print)1314-2100

Conference

Conference13 INTERNATIONAL CONFERENCE ON
COMMUNICATIONS, ELECTROMAGNETICS AND MEDICAL
APPLICATIONS (CEMA’18)
Abbreviated titleCEMA’18
Country/TerritoryBulgaria
CityСофия
Period18/10/1820/10/18
Internet address

    Scopus subject areas

  • Computer Science(all)

ID: 35296033