Journal Article

·2017

Classification of ships with fuzzy logic by using radar echo

Burak Alptekin YTU , Bilal Kocakusaklar YTU , Nihan Kahraman YTU

Abstract

Nowadays, the range of guided missiles used on military ships has increased considerably. This has led to the need for military vessels to classify contacts with high accuracy from distant ranges. For Coast Guard vessels, it is necessary to classify vessels in order to find wanted ships, illegal hunting, pirates and illegal vessels. The right of way on the sea varies greatly depending on the class of ship. The shipman must classify the vessel in front of him so that he can decide whether or not he has the right of way. In short, all vessels in motion need to classify the vessels they see on the radar. Radar and electronic maps are used as navigational aid and navigation safety in ships. The contacts followed by the radar are instantly transferred to the electronic map. Changes in these contacts over time give us a lot of information about the contact (route change, speed change, speed, commercial line follow-up, echo intensity, geometric shape of the etc.). The method developed is classed by fuzzy logic, using only the properties of the radar echo. With the method, all the ships followed by the radar are classified as highly accurate. The proposed method is considered to reduce the workload of expert personnel in case of being used in military ships, coast guard ships and civil ships.

Keywords

Radar Coast guard Echo (communications protocol) Guard (computer science) Computer science Fuzzy logic Radar imaging Artificial intelligence Marine engineering Aeronautics Computer vision Engineering Telecommunications Computer security

Subject Areas

Maritime Navigation and Safety ·Ocean Engineering ·Physical Sciences
Structural Integrity and Reliability Analysis ·Mechanical Engineering ·Physical Sciences
Ship Hydrodynamics and Maneuverability ·Ocean Engineering ·Physical Sciences

OpenAlex SDG Match

SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).

Life below water 69%