Modelling Aircraft Disturbance Fields for Magnetic Navigation Using Dense ANNs and the Novel MANNTL Architecture

Abstract

The ability to use GPS for navigation is becoming increasingly limited in certain areas of the world. Knowing this, the Air Force Research Labs is constantly looking for ways to improve alternate navigation methods such as magnetic navigation. In the interest of making advancements in aircraft disturbance xC;field modelling, Lieutenant Emery recreates models from previous works to prove results. Lieutenant Emery also introduces a novel model architecture that attempts to mix the xC;filtering properties of Tolles-Lawson with the non-linear capabilities of an artifixC;cial neural network. The introduction of this model could present better aircraft disturbance xC;field modelling and in turn, more reliable magnetic navigation in regions where GPS is not available.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1132393

Entities

People

  • Kyle A. Emery

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Artificial Intelligence Software
  • Convolutional Neural Networks
  • Environment
  • Global Positioning Systems
  • Inertial Navigation
  • Inertial Navigation Systems
  • Information Science
  • Machine Learning
  • Magnetic Fields
  • Magnetic Navigation
  • Measurement
  • Military Research
  • Navigation
  • Neural Networks
  • Recurrent Neural Networks
  • Supervised Machine Learning
  • United States
  • Unmanned Aerial Vehicles
  • Vector Magnetometers

Readers

  • Computational Fluid Dynamics (CFD)
  • Military History of the United States in the 20th Century.
  • Positioning, Navigation, and Timing (PNT) Technology.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space