Paper
28 April 2010 Capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data
Steven P. Brumby, Kary L. Myers, Norma H. Pawley
Author Affiliations +
Abstract
Many problems in electromagnetic signal analysis exhibit dynamics on a wide range of time scales. Further, these dynamics may involve both continuous source generation processes and discrete source mode dynamics. These rich temporal characteristics can present challenges for standard modeling approaches, particularly in the presence of nonstationary noise and clutter sources. Here we demonstrate a hybrid algorithm designed to capture the dynamic behavior at all relevant time scales while remaining robust to clutter and noise at each time scale. We draw from techniques of adaptive feature extraction, statistical machine learning, and discrete process modeling to construct our hybrid algorithm. We describe our approach and present results applying our hybrid algorithm to a simulated dataset based on an example radio beacon identification problem: civilian air traffic control. This application illustrates the multi-scale complexity of the problems we wish to address. We consider a multi-mode air traffic control radar emitter operating against a cluttered background of competing radars and continuous-wave communications signals (radios, TV broadcasts). Our goals are to find a compact representation of the radio frequency measurements, identify which pulses were emitted by the target source, and determine the mode of the source.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven P. Brumby, Kary L. Myers, and Norma H. Pawley "Capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data", Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 771002 (28 April 2010); https://doi.org/10.1117/12.850199
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Cited by 2 scholarly publications.
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KEYWORDS
Chemical species

Feature extraction

Detection and tracking algorithms

Electromagnetism

Process modeling

Signal to noise ratio

Associative arrays

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