Accession Number:
AD1058343
Title:
Approximate Morphism via Machine Learning for an Electronic Warfare Simulation Component
Descriptive Note:
Technical Report,01 Apr 2017,31 Jul 2017
Corporate Author:
NAVAL RESEARCH LAB WASHINGTON DC WASHINGTON United States
Personal Author(s):
Report Date:
2018-08-14
Pagination or Media Count:
23.0
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.
Descriptors:
Subject Categories:
- Countermeasures
- Numerical Mathematics