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Electromagnetic Interference Estimation Via Conditional Neural Processing Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 12-2020 Electromagnetic Interference Estimation via Conditional Neural Processing Edgar E. Gomez Follow this and additional works at: https://scholar.afit.edu/etd Part of the Electromagnetics and Photonics Commons Recommended Citation Gomez, Edgar E., "Electromagnetic Interference Estimation via Conditional Neural Processing" (2020). Theses and Dissertations. 4537. https://scholar.afit.edu/etd/4537 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. Electromagnetic Interference Estimation via Conditional Neural Processing THESIS Edgar E. Gomez AFIT-ENG-MS-20-D-006 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENG-MS-20-D-006 Electromagnetic Interference Estimation via Conditional Neural Processing THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Engineering Edgar E. Gomez, B.S.Cp.E. November 27, 2020 DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENG-MS-20-D-006 Electromagnetic Interference Estimation via Conditional Neural Processing THESIS Edgar E. Gomez, B.S.Cp.E. Committee Membership: Maj Joseph A. Curro, Ph.D Chair Lt Col James W. Dean, Ph.D Member Richard K. Martin, Ph.D Member AFIT-ENG-MS-20-D-006 Abstract The goal of this thesis is to determine the efficacy of employing Machine Learn- ing (ML) to solve Joint Urgent Operational Need (JUON) CC-0575, which aims to develop a Common Operating Picture (COP) of the Global Positioning System (GPS) Electromagnetic Interference (EMI) environment. With the growing popular- ity of Artificial Neural Networks (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 (single, double, and triple jammer datasets). The type of ANN developed to address this problem is a Conditional Neural Process (CNP) with residual connec- tions. 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. The model resulted in an EMI estimator that was neutral on the single jammer test data set, yet aggressive on the multiple jammer test data sets. iv Table of Contents Page Abstract . iv List of Figures . vii List of Tables . xi I. Introduction . .1 1.1 Problem Background. .1 1.2 Research Objectives . .1 1.3 Document Overview . .2 II. Background and Literature Review . .3 2.1 Global Positioning System . .3 2.1.1 Space Segment . .3 2.1.2 Control Segment . .5 2.1.3 User Segment . .6 2.2 GPS Receiver Functionality . .6 2.2.1 Antenna . .7 2.2.2 Preamplifier . .8 2.2.3 Down-converter . .8 2.2.4 Reference Oscillator and Frequency Synthesizer . .8 2.3 GPS Signal Interference . .8 2.3.1 Unintentional Interference . .9 2.3.2 Intentional Interference . .9 2.4 Interference Detection and Geolocation . 10 2.5 Machine Learning . 11 2.6 Deep Learning and Artificial Neural Networks . 13 2.6.1 Perceptron . 14 2.6.2 Activation Functions . 15 2.6.3 Loss Functions . 16 2.6.4 Residual Connections . 17 2.6.5 Conditional Neural Processing . 18 2.7 Systems Tool Kit . 18 2.7.1 Scenarios . 19 2.7.2 Objects . 19 2.7.3 Reports . 19 2.7.4 External Control and Automation . 19 v Page III. Methodology . 21 3.1 Data Simulation and Collection . 21 3.1.1 Scenario Description . 22 3.1.2 WSMR Dataset . 27 3.1.3 Data Collection Automation . 29 3.2 ANN Architecture . 29 3.3 ANN Training . 32 3.3.1 Custom Loss and Activation Functions . 32 3.3.2 Hyper-Parameter Selection . 32 3.3.3 Training Details . 33 3.4 Methods of Analysis . 34 3.4.1 Mean, Variance, and Mahalanobis Distance . 34 3.4.2 Q-Q Plot . 39 IV. Results and Analysis . 41 4.1 Hyper-Parameter Selection Results . 41 4.2 Training Results and Analysis . 42 4.3 Test Description. 49 4.4 Test Results and Analysis . 49 4.4.1 Single Jammer Case . 50 4.4.2 Double Jammer Case . 55 4.4.3 Triple Jammer Case . 59 4.5 Results Discussion . 63 V. Conclusions . 69 5.1 Future Work . 69 Bibliography . 71 Acronyms . 74 vi List of Figures Figure Page 1 Expandable 24-Slot GPS Satellite Constellation . .4 2 Control Segment Layout . .5 3 GPS Receiver Functional Block Diagram . .7 4 Artificial Intelligence, Machine Learning, and Deep Learning . 11 5 Machine Learning Paradigm. 12 6 Single Layer Neural Network . 13 7 Perceptron Graphical Representation . 14 8 Simplified Perceptron Representation . 15 9 Examples of Non-Linear Activation Functions . 16 10 Residual Network Representation . ..
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