Electromagnetic Interference Estimation via Conditional Neural Processing
Abstract
The goal of this thesis is to determine the efficacy of employing ML to solve JUON CC-0575, which aims to develop a COP of the GPS EMI environment. With the growing popularity of ANNs, ML solutions are quickly gaining traction in businesses, academia and government. This in turn allows for problem solutions that were previously inconceivable using the classical programming paradigm. This thesis proposes a method to develop a COP of the battlefield via ANN ingestion of multiple-source signals and sensors. We conduct three separate experiments with varying amounts of EMI interference sources. The type of ANN developed to address this problem is a CNP with residual connections. The model is developed to provide the estimated EMI environment as well as a measure of confidence in its estimates, as the specific application of this model could lead to loss of life in the event the model estimates are taken as truth.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 27, 2020
- Accession Number
- AD1126994
Entities
People
- Edgar E. Gomez
Organizations
- Air Force Institute of Technology