DocumentCode :
2879144
Title :
Data fusion analysis for maritime automatic Target Recognition with designation confidence metrics
Author :
Sathyendra, Harsha M. ; Stephan, Bryan D.
Author_Institution :
Raytheon Co., McKinney, TX, USA
fYear :
2015
fDate :
10-15 May 2015
Abstract :
This research encompasses robust data fusion methodology for maritime target feature extraction and combinatorial classifiers (Vector Quantizer, Neural Networks based on both Gaussian Mixture Models and Radial Basis Function), which also includes confidence metrics for designations. Feature extraction techniques act on 2-d Inverse Synthetic Aperture Radar (ISAR) images and novel 1-d range profiles. Designations are made with an ISAR database of over 2500 images and 8 basic classes. The Fusion classifier confusion-matrix results indicate the correct classification probability of 80.1% and perfect joint-classification designation in the rarer instances where 2 similar classes are too hard to distinguish from.
Keywords :
Gaussian processes; feature extraction; image classification; mixture models; object recognition; radar imaging; synthetic aperture radar; 2D inverse synthetic aperture radar image; Gaussian mixture models; ISAR image; combinatorial classifiers; data fusion analysis; designation confidence metrics; maritime automatic target recognition; maritime target feature extraction; neural networks; radial basis function; vector quantizer; Databases; Engines; Feature extraction; Graphical user interfaces; Radar imaging; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Radar Conference (RadarCon), 2015 IEEE
Conference_Location :
Arlington, VA
Print_ISBN :
978-1-4799-8231-8
Type :
conf
DOI :
10.1109/RADAR.2015.7130971
Filename :
7130971
Link To Document :
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