Satellite Telemetry

3:00 PM – Integrating Manned Control Systems and Camera Feeds for Aerial Vehicle Control
Ahmad Qureshi, Cole Hersh, Drake Russ, Ryan Fong, Alexia Segarra, Lilly Gentry, Aarush Parvataneni, Etan Grant and Dr. Michael Marcellin, University of Arizona

We present a novel integration of Pixhawk autopilot technology with a manual control system utilizing a live camera feed for real-time mission control. TMotor FLAME and HobbyWing PLATINUM motors provide a powerful and stable source of lift and thrust. The Pixhawk (PX4) autopilot system provides a robust platform for autonomous missions in unmanned aerial vehicles (UAVs), offering precise control and navigation capabilities. Utilizing a Python library to transmit and interpret MAVlink messages, the aircraft responds to commands with meter-level accuracy. Instead of autonomous processing, the system relies on a real-time camera feed to provide operators with visual information, enabling manual adjustments during flight. This allows for responsive control in dynamic environments, ensuring adaptability to obstacles and changing conditions. We aim to highlight the synergy between the PX4 and manual control systems, demonstrating their combined potential to enhance UAV operations through real-time human oversight.

3:20 PM – Enhancing CubeSat Telemetry Systems for Autonomous Space Missions Utilizing Machine Learning Techniques
Connor Looney, Ethan Wenger, Dr. Adam Gorrell and Dr. Erik Perrins, University of Kansas

A CubeSat is a valuable tool used by many organizations, including NASA, who partners with universities to design and build satellites for data collection. A primary challenge for CubeSats is maintaining reliable telemetry during autonomous operations. The objective of this paper is to present a machine learning-driven approach to improve real-time data analysis and anomaly detection. The proposed algorithm has the potential to improve the decision-making and reliability of the CubeSat telemetry system, while addressing its unique constraints. The machine learning algorithm, incorporating data supplied by Attitude Determination and Control System (ADCS) components, could find new avenues to increase the efficiency of satellite reorientation based on supplied attitude determination data. Enhancements to the CubeSat operating system could allow for more effective research of autonomous space missions.

3:40 PM – Unsupervised Clustering of  Anomalous Samples of Time-Series Data Using Anomaly-Detection-Capable Statistical Features
Nischay Rawal, Bryson Sanders, Rishabh Anand, Dr. John Paden, Dr. Adam Gorrell and Dr. Erik Perrins, University of Kansas

Dependable data quality in satellite telemetry is critical for the reliability of space missions. CubeSats such as the University of Kansas’ KUbeSat-1 encounter various anomalies from harsh space environments and hardware degradation. Traditional threshold-based anomaly detection models fail to differentiate error sources within telemetry data. Despite existing methods, there remains a need for a machine learning based model that systematically categorizes telemetry anomalies. This study proposes an unsupervised learning approach that leverages historical telemetry data to recognize characteristic anomaly signatures. The model clusters segments into distinct behavioral groups using a KMeans algorithm, which are assessed through dimensionally reduced visualizations and silhouette scores. This pipeline enhances CubeSat reliability by supporting scalable, telemetry monitoring frameworks that enable smarter design choices and resource allocation for future small satellite missions.

4:00 PM – Spectrum Anomalies on Small Satellite LEO to GEO Relay Links
Dr. Brian Kopp, Jacksonville University; Brett Betsill and Matt Taylor, Microcom Environmental; Marcus Murbach, NASA Ames Research Center; Beau Backus, JHU APL for NOAA

Working in partnership, NOAA, NASA and EUMETSAT are investigating the feasibility of implementing small satellite remote sensing in low-earth orbit (LEO) that utilizes the Data Collection System (DCS) for data relay. The small satellite remote sensing platform would use a DCS transponder onboard the NOAA GOES spacecraft as a relay to earth for conveyance to the user. Two successful experimental payloads on two NASA Ames technology education satellites have been flown so far. The initial tests, while promising and meeting mission goals, have been impacted by spectrum anomalies, that appear alongside the desired signal in spectrograms. The characteristics of the anomalies suggest they are not generated by the spacecraft. The most likely explanation for their presence appears to be doppler-shifted, multipath earth reflections. This is believed to be one of the first cube satellite LEO-to-GEO-to earth relay links to be studied and the spectrum anomalies remain an open research topic.