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
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;
Conference_Titel :
Radar Conference (RadarCon), 2015 IEEE
Conference_Location :
Arlington, VA
Print_ISBN :
978-1-4799-8231-8
DOI :
10.1109/RADAR.2015.7130971