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Accession Number:
AD1058343
Title:
Approximate Morphism via Machine Learning for an Electronic Warfare Simulation Component
Report Date:
2018-08-14
Abstract:
Electromagnetic waveforms are an essential component of high-fidelity radar and electronic warfare digital computer simulations. Sampled representations of radar waveforms are widely used for their physical realism and suitability for algorithimic processing. However, this fidelity comes at a price because operations on radar waveforms are often a computationally costly simulation bottleneck. In this report, we propose a method for constructing a reduced, feature-based alternative radar waveform model component derived from a given high-fidelity component. The resulting model will be related to the original through an approximate morphism. The proposed method is illustrated with a highly simplified waveform model. Both linear and nonlinear approaches are considered; in particular, a role for machine learning techniques is identified.
Document Type:
Conference:
Journal:
Pages:
23
File Size:
0.96MB
Contracts:
Grants:
Distribution Statement:
Approved For Public Release
