Pedestrian Navigation using Artificial Neural Networks and Classical Filtering Techniques
Technical Report,01 Sep 2018,26 Mar 2020
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States
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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.
- Computer Systems
- Navigation and Guidance