Classification of transient signals using sparse representations over adaptive dictionaries

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 8058, Issue , 2011, Pages

  Moody, Daniela I ^a,b   Brumby, Steven P ^a   Myers, Kary L ^a   Pawley, Norma H ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

dictionary learning; RF transient classification; sparse representation

Indexed keywords

ADAPTIVE CLASSIFICATION; ADDITIONAL KNOWLEDGE; ANALYTICAL MODEL; AUTOMATIC CLASSIFICATION; CLUTTERED ENVIRONMENTS; COMPUTATIONAL COSTS; COMPUTATIONAL OVERHEADS; DICTIONARY LEARNING; FEATURE EXTRACTION AND CLASSIFICATION; FEATURE SELECTION ALGORITHM; FOURIER; FUNCTION CLASS; NOISE LEVELS; PERSISTENT SURVEILLANCE; RADIOFREQUENCY SIGNALS; REDUNDANT DICTIONARIES; RF TRANSIENT CLASSIFICATION; SIGNAL CHARACTERISTIC; SIGNAL CLASS; SPARSE CLASSIFICATION; SPARSE DECOMPOSITION; SPARSE REPRESENTATION; TARGET CLASSIFICATION; TARGET SIGNALS; TIME-FREQUENCY SPACE; TRANSIENT SIGNAL;

BIOLOGICAL SYSTEMS; FEATURE EXTRACTION; MATHEMATICAL MODELS; TRANSIENTS; WAVELET TRANSFORMS;

NEURAL NETWORKS;

EID: 79960078128     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.883341     Document Type: Conference Paper

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