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Title:

A Dempster-Shafer Rule Based Method to Distinguish ARPA Targets with Their Motion Features

Accession Number:

01551246

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

The ARPA, Automatic Radar Plotting Aid Targets, is a standard feature which is able to detect moving vessel and widely supported by marine radars, however, it often complicates the noise and channel structures into vessels and confuses the navigators. With the help of speed, course and position characteristics, it is possible to build an artificial intelligence based on Dempster-Shafer rule to distinguish the targets. The Dempster-Shafer rule is adopted for its powerful characters of combining all the evidences, which is similar to the procedure of manual judgment. In the field testing, the new approach is able to distinguish the vessel targets at the accuracy of 88.1%, which is helpful for the navigation and maritime safety.

Supplemental Notes:

Alternate title: Dempster-Shafer Rule-Based Method to Distinguish Automatic Radar Plotting Aid Targets with Their Motion Features. This paper was sponsored by TRB committee AW040 Marine Safety and Human Factors.

Monograph Accession #:

01550057

Report/Paper Numbers:

15-2795

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Ma, Feng
Liu, Xinglong

Pagination:

19p

Publication Date:

2015

Conference:

Transportation Research Board 94th Annual Meeting

Location: Washington DC, United States
Date: 2015-1-11 to 2015-1-15
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; Photos; References; Tables

Identifier Terms:

Subject Areas:

Data and Information Technology; Marine Transportation; Operations and Traffic Management; I70: Traffic and Transport

Source Data:

Transportation Research Board Annual Meeting 2015 Paper #15-2795

Files:

TRIS, TRB, ATRI

Created Date:

Dec 30 2014 12:57PM