A fuzzy inference system for ship-ship collision alert generation

Constantinos Rizogiannis; Stelios C. A. Thomopoulos

doi:10.1117/12.2519475

7 May 2019 A fuzzy inference system for ship-ship collision alert generation

Constantinos Rizogiannis, Stelios C. A. Thomopoulos

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 110180B (2019) https://doi.org/10.1117/12.2519475
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Ship-ship collisions are rare events mainly caused by human errors. In an ever growing global trade world, the avoidance of such accidents is of great importance as they may cause significant human, environmental, and financial consequences. A collision warning system providing to Maritime related authorities (Vessel Traffic Services, Coast Guard authorities, Port authorities, other) real-time alerts for ship-ship encounter may significantly contribute to the reduction of collision accidents and the increase of navigation safety. In this paper a Fuzzy inference system (FIS) is proposed to estimate risk collision for pairs of ships receiving as input parameters calculated based on data obtained from the Automatic Identification System (AIS) which is installed onboard ships. The proposed system operates in real time examining pairs of ships that recently transmitted an AIS signal while discards, at an early processing stage, those pairs which do not satisfy specific conditions in order to reduce computational burden. Experiments have been carried out with real data and the results show that the proposed approach is effective in automatic alert generation for ships involved in near ship-ship situations.

Citation Download Citation

Constantinos Rizogiannis and Stelios C. A. Thomopoulos "A fuzzy inference system for ship-ship collision alert generation", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180B (7 May 2019); https://doi.org/10.1117/12.2519475

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available