Telemetry Data Mining for Unmanned Aircraft Systems

Abstract

With ever more data becoming available to the US Air Force, it is vital to develop effective methods to leverage this strategic asset. Machine learning (ML) techniques present a means of meeting this challenge, as these tools have demonstrated successful use in commercial applications. For this research, three ML methods were applied to a unmanned aircraft system (UAS) telemetry dataset with the aim of extracting useful insight related to phases of flight. It was shown that ML provides an advantage in exploratory data analysis and as well as classification of phases. Neural network models demonstrated the best performance with over 90 accuracy in classifying of UAS phases of flight. Categorical and Regression Trees (CART) also performed well, whereas C5.0 is less suited for this task. In addition, several interesting patterns were uncovered within the dataset, which can aid UAS operators in identifying mission anomalies and atypical system operation.

Document Details

• Document Type: Technical Report
• Publication Date: Mar 01, 2022
• Accession Number: AD1174742

Entities

People

• Li N Yu

Organizations

• Air Force Institute of Technology

Tags

Communities of Interest

• Air Platforms
• Autonomy
• C4I
• Electronic Warfare
• Energy and Power Technologies
• Space

DTIC Thesaurus Topics

• Air Force
• Aircraft Equipment
• Aircrafts
• Airframes
• Anti-Tank Missiles
• Artificial Intelligence Software
• Data Analysis
• Data Mining
• Engineering
• Factor Analysis
• Ground Control Stations
• Information Science
• Machine Learning
• Neural Networks
• Systems Engineering
• Unmanned Aerial Systems
• Unmanned Aerial Vehicles

Readers

• Aerospace Test and Evaluation
• Neural Network Machine Learning.
• Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

• AI & ML
• AI & ML - Autonomous Systems
• AI & ML - DoD AI Strategy
• AI & ML - Neural Networks
• Autonomy