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Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals
Joint Authors
Protopapadakis, Eftychios
Voulodimos, Athanasios
Doulamis, Nikolaos
Doulamis, Anastasios
Dres, Dimitrios
Bimpas, Matthaios
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications.
High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances.
However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy.
In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar.
The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering.
A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance.
American Psychological Association (APA)
Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Anastasios& Doulamis, Nikolaos& Dres, Dimitrios& Bimpas, Matthaios. 2017. Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141029
Modern Language Association (MLA)
Protopapadakis, Eftychios…[et al.]. Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1141029
American Medical Association (AMA)
Protopapadakis, Eftychios& Voulodimos, Athanasios& Doulamis, Anastasios& Doulamis, Nikolaos& Dres, Dimitrios& Bimpas, Matthaios. Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141029
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141029