Pedestrian Navigation using Artificial Neural Networks and Classical Filtering Techniques

Abstract

The objective of this thesis is to explore the improvements achieved through using classical filtering methods with Artificial Neural Network (ANN) for pedestrian navigation techniques. A novel urban data set is created for testing various localization and Pedestrian Dead Reckoning (PDR) based pedestrian navigation solutions. Cell phone data is collected including images, accelerometer, gyroscope, and magnetometer data to train the ANN. The ANN methods are explored first trying to achieve a low Root Mean Squared Error (RMSE) of localization and PDR solutions. After analyzing the localization and PDR solutions they are combined into an Extended Kalman Filter to achieve a 20 reduction in the RMSE.

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Document Details

Document Type
Technical Report
Publication Date
Mar 19, 2020
Accession Number
AD1104226

Entities

People

  • David J. Ellis

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Artificial Neural Networks
  • Automata Theory
  • Computational Science
  • Computer Vision
  • Computers
  • Data Mining
  • Databases
  • Dead Reckoning
  • Detectors
  • Global Positioning Systems
  • Image Processing
  • Information Science
  • Kalman Filters
  • Machine Learning
  • Mobile Phones
  • Navigation
  • Network Architecture
  • Network Science
  • Neural Networks
  • Supervised Machine Learning
  • World Geodetic System

Readers

  • Inertial Navigation Systems.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks