Capturing dynamics on multiple time scales: A hybrid approach for cluttered electromagnetic data

Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales against nonstationary clutter and noise. We consider a problem in which the relevant time scales can range from nanoseconds to hours or days (12 or 13 orders of magnitude). We present a hybrid algorithm...

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Bibliographic Details
Main Authors: Pawley, Norma H, Myers, Kary L, Galbraith, John M, Brumby, Steven P
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Chirp
Feature extraction
Machine learning
Machine learning algorithms
Noise level
Noise robustness
Signal analysis
Signal processing
White noise
Online Access:Request full text
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Summary:Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales against nonstationary clutter and noise. We consider a problem in which the relevant time scales can range from nanoseconds to hours or days (12 or 13 orders of magnitude). We present a hybrid algorithm currently designed to capture the dynamic behavior at scales from nanoseconds to milliseconds (6 orders of magnitude) while remaining robust to clutter and noise. We draw from techniques of adaptive feature extraction, statistical machine learning, and discrete process modeling and present results on a simulated multimode problem. Our goals are to find a representation of the signal that allows us to identify which pulses were produced by a target emitter and to determine the operational mode of the target.
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.2009.5469701