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.

Subject Categories:

  • Countermeasures
  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE