Approximate Morphism via Machine Learning for an Electronic Warfare Simulation Component

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 Details

• Document Type: Technical Report
• Publication Date: Aug 14, 2018
• Accession Number: AD1058343

Entities

People

• Donald E. Jarvis

Organizations

• United States Naval Research Laboratory

Tags

Communities of Interest

• Autonomy
• Electronic Warfare

DTIC Thesaurus Topics

• Algorithms
• Artificial Neural Networks
• Automata Theory
• Computational Science
• Computer Programming
• Computer Programs
• Computer Science
• Computers
• Control Systems
• Data Mining
• Digital Data
• Digital Signal Processing
• Dimensionality Reduction
• Doppler Effect
• Electronic Warfare
• Information Science
• Machine Learning
• Multiagent Systems
• Neural Networks
• Reliability
• Signal Processing
• Simulations
• Software Development
• Supervised Machine Learning
• Two Dimensional
• Unsupervised Machine Learning

Readers

• Computational Modeling and Simulation
• Neural Network Machine Learning.
• Radar Systems Engineering.

Technology Areas

• AI & ML
• AI & ML - Bayesian Inference
• AI & ML - Machine Learning Algorithms
• Microelectronics