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Proceedings Paper

Automated radar behavior analysis using neural network architectures
Author(s): Gary Whittington; C. Tim Spracklen; J. M. Haugh; Helen Faulkner
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Paper Abstract

In this paper the application and performance of Artificial Neural Networks (ANN) to the problem of sensor data fusion is reported for an experimental system, Tracker. The task of sensor data fusion involves integrating numerous data streams, originating from disparate sensors, into a consistent model that represents the pertinent higher level features of the environment as well as presenting an assessment of their significance. In the case of the modern naval environment, the problem central to many tactical data fusion systems is the need for rapid acquisition and interpretation of the information. In a potentially hostile situation the time taken to perform such an assessment is severely limited and a rapid and accurate response is vital. This paper describes the application of ANN to tactical sensor data fusion and the automated processing of the radar behaviors for various vehicle types. In particular the tasks of target and behavioral identification for both automated surveillance and support tasks are highlighted as important in the modern naval environment. The experimental research program divided the analysis of the radar tracks into three distinct categories. These were (1) target identification, (2) behavioral analysis (target task identification), and (3) threat assessment.

Paper Details

Date Published: 19 August 1993
PDF: 16 pages
Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); doi: 10.1117/12.152615
Show Author Affiliations
Gary Whittington, Univ. of Aberdeen (United Kingdom)
C. Tim Spracklen, Univ. of Aberdeen (United Kingdom)
J. M. Haugh, Defence Research Agency (United Kingdom)
Helen Faulkner, Ferranti International PLC (United Kingdom)


Published in SPIE Proceedings Vol. 1966:
Science of Artificial Neural Networks II
Dennis W. Ruck, Editor(s)

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