RSS 1.0International Telemetering Conference Proceedings, Volume 60 (2025)
ABOUT THE COLLECTION
The International Telemetering Conference/USA (ITC/USA) is dedicated to the promotion and stimulation of technical growth in telemetering and its allied arts and sciences. It is the premier annual forum and technical exhibition providing telemetry specific short courses, technical papers from professionals and students, and exhibits of the industry’s leading companies. ITC/USA is sponsored by the International Foundation for Telemetering (IFT), a non-profit corporation dedicated to serving the technical and professional interests of the telemetering community.
This collection contains the proceedings of The Sixtieth Annual International Telemetering Conference and Technical Exhibition, October 20-23, 2025. The conference, sponsored by the International Foundation for Telemetering, was held at the Horseshoe Las Vegas, Nevada.
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International Telemetering Conference Proceedings, Volume 60 (2025)International Foundation for Telemetering, 2025-10
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LEVERAGING PILOT SEQUENCE ORTHOGONALITY FOR LOW COMPLEXITY SPACE-TIME CODING RECEIVERSThis paper explores CPM waveforms used for telemetry purposes, with a specific focus on SOQPSK TG standard and its integration with Space-Time Coding to address the two-antenna problem. Cur rent state-of-the-art receiver algorithms face challenges, including delays, error propagation, and poor performance at low SNR levels. To address these issues, this paper introduces a novel re ceiver design leveraging improved correlation properties of new pilot sequences. The proposed design performs CSI estimation at the pilot detection stage, thus eliminating iterative processes, mitigating long synchronization times, and enhancing receiver performance at lower SNR levels.
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Telemetry Spectrum Encroachment Review of Related WRC-27 Agenda and Domestic ThreatsA review of international and domestic spectrum issues that present challenges for the future use of radio frequency telemetry after the 2023 World Radiocommunications Conference (WRC-23). Several agenda items addressed there have a potential for telemetry spectrum encroachment and are presented in this paper. Agenda topics of telemetry interest in future World Radiocommunications Conferences (WRCs), in particular several agenda items that will be considered at the 2027 World Radiocommunications Conference (WRC-27), are a particular focus. International telemetry vendors, suppliers, and users need to be aware of, and potentially engage with, their national administrations on these items to protect and preserve spectrum for the future of aeronautical mobile telemetry.
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ELIMINATING UNDETECTED ERRORS IN LDPC–CPM DECODERSIn error control coding, an undetected error occurs when the received code word passes the parity check even though errors are present. Such errors are a critical concern in many applications because a passed parity check at the receiver is taken to mean that the received code word is error free. In this paper, we outline the key operating principles and differences between the two constituent decoders in the system. Our high-level discussion reveals insights on two strategies that can be used in an LDPC–CPM decoder to eliminate undetected errors. The first is a brute-force approach of performing an additional decoding iteration to confirm (or rebut) a passed parity check. The second is a simple and elegant check-node-splitting approach that can be incorporated into the LDPC code itself. We provide numerical results and operating scenarios where these techniques are recommended for use.
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DENSITY EVOLUTION ANALYSIS OF PUNCTURED IRIG-106 LDPC CODES: TOWARD CONTESTED SPECTRUM RESILIENCYRecently, random puncturing was shown to be a promising solution to reduce the coding over head and improve the spectral efficiency of low-density parity-check (LDPC) codes designed for the continuous phase modulation (CPM) waveforms of the Advanced-Range TeleMetry (ARTM) program. In this paper, we perform an asymptotic iterative decoding analysis of punctured ARTM LDPC codes. We determine iterative decoding thresholds for this family of codes for structured and unstructured puncturing patterns. Prudent selection of puncturing patterns will allow us to create spectrally efficient codes of various rates across the target operational regime. We show that it is possible to identify puncturing patterns in the underlying code protographs that result in catastrophic iterative decoding failure, thereby informing the combinatorial search for robust puncturing schemes that preserve performance while enabling tactical rate adaptation.
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OTFS MODULATION FOR THE AMT ENVIRONMENTOrthogonal Time Frequency Space (OTFS) is a new modulation designed to operate in the high mobility/high-Doppler environment that is closely related to 5G’s Orthogonal Frequency Division Multiplexing (OFDM). OFDM has been investigated for use in the next generation telemetry sys tems, but performance can suffer at high test article (TA) speeds due to rapid variations in the time-frequency channel response and inter-carrier interference (ICI) resulting from Doppler shifts. This paper presents an overview of OTFS and an exemplary OTFS system that can operate at high TA speeds in the Aeronautical Mobile Telemetry (AMT) environment to showcase advantages over OFDM such as resistance to ICI and jamming, lower peak to average power ratio (PAPR), and better spectral efficiency. Future areas of research unique to the AMT use case are discussed.
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IMPROVING RCC 118 TEST METHODS FOR DQE/DQM BASED ON INDUSTRY DAY 2 RESULTSA test event, based on RCC 118 Volume 2 Chapter 11, Test Methods for Assessing Telemetry Receiver Data Quality Metrics, was conducted in October 2024. All six tests including AWGN, step and dwell, ACI, two types of multipath, and resynchronization were performed with receiver equipment from multiple vendors. The lessons learned from conducting these tests provided valuable insight into significantly improving the existing test methods. This paper describes the methods and procedures used for the Industry Day 2 testing, an overview of the results, and proposes changes to the published test methods.
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WICKED: DIGITAL INTERFACE CONTROL DOCUMENTSDigital interfaces are often defined in text-based documents such as PDF, necessitating time intensive and error-prone human-in-the-loop transcription for each implementation of the interface. The lack of standardized language between interface control documents (ICDs) inhibits common tooling and multi-system analysis for DoD Large Force Test Events (LFTEs), and the unstructured text definitions are prone to logical, structural, and typographic errors. The 309th Software Engineering Group (SWEG) has developed a human- and machine-readable digital ICD standard which enables unified tools for translation, data management, and analysis of dissimilar digital systems. The format is defined with a descriptive, enforceable, and extensible schema, which guarantees well-formed ICDs, while allowing bespoke application-specific behav iors. Adjacent SWEG-developed tools for parsing PDF formats have extracted over 40 digital ICDs for various DoD systems, generating over 1 million lines of code.
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FURTHER ADVANCES IN DEVELOPING A UNIFIED POST FLIGHT ANALYSIS SYSTEMThe IADS team has been designing a unified platform for post-test analysis over the past few years. This design aimed to satisfy a set of core requirements that were gathered from meeting with engineering groups throughout the flight test community. The goal was to create a standardized system for post-test analysis from those requirements that could be used by different flight test disciplines across projects. The IADS team has made significant progress implementing these requirements. We have also received valuable feedback from the flight test community on ways to improve the system. This paper will explore some of the challenges encountered using such a system in a real-world environment, as well as some solutions that have been used to overcome these obstacles. Topics will include strategies for interfacing with different data file formats, challenges faced when working with data in the cloud, adding additional flexibility to data analysis, and user-interface improvements.
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TELEMETRY-DRIVEN ELECTRICAL SYSTEM TO OPTIMIZE ENGINE PERFORMANCEThe Wildcat Formula Racing (WFR) team of the University of Arizona designed and built an electrical system for the team’s race car which requires a telemetry system to tune the engine. The electrical system was based upon previous designs and focused on accurately reading data from the various sensors to the ECU (Electronic Control Unit). Such sensors include the manifold absolute pressure sensor, throttle position sensor, air temperature sensor, etc. The ECU calculates the data into a readable graph which the team takes into account to optimize for peak performance. Through the integration of telemetry, the team is able to monitor the engine and ensure maximum efficiency.
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HARDWARE-SOFTWARE CO-DESIGN OF INTEGRATIVE TELEMETRY SYSTEM FOR OFF-ROAD RACING VEHICLEThe University of Arizona Baja Wildcat Racing Team’s integrative off-road telemetry system merges custom circuit boards and a C#-based, GPU-accelerated GUI. Utilizing CAN-bus, I2C, and SPI protocols alongside NRF24L01 devices for wireless communication, it collected data via IMUs, temperature sensing devices, three speed sensors, an induction-based tachometer using spark plug-driven input, and pressure transducers. The system monitored RPMs, positional data, and brake actuation, enhancing real-time data visualization through an improved dashboard interface. Hardware and software advancements significantly refined telemetry accuracy and driving insights, optimizing vehicle performance. This innovation demonstrates a leap forward in the university’s off-road racing telemetry.
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AUTONOMOUS CONTROL IN BEETLEWEIGHT COMBAT ROBOTICS USING COMPUTER VISIONBeetleweight Combat Robotics is an event-driven sport consisting of two or more opposing, three pound robots in an enclosed arena, traditionally remotely controlled by a pilot. A novel alternative to the standard human pilot is an autonomous control algorithm using computer vision via a top down orthographic camera. A closed-loop control algorithm is implemented, in which robots in an arena are detected and subsequent motion correction is calculated and relayed to the autonomous robot. Autonomous control carries the benefit of removing human reaction time while increasing command frequency, which improves the resolution of control over a standard human pilot. A Combat Robotics arena provides a consistent testing ground for autonomous control where com plex object interaction is analyzed with computer vision. Consequently, this experience enables motivated engineers in the Wildcat Robotics Club at the University of Arizona to gain experience with computer vision and develop the skills required to advance the state of self driving technology in motorized vehicles.
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BYU MARS ROVER TELEMETRY IN THE 2025 UNIVERSITY ROVER CHALLENGEAs part of a senior-level capstone design course, our team designed, developed, and built a planetary rover for the University Rover Challenge (URC), capable of autonomous and manual remote operation. This paper presents and discusses the role of telemetry systems in facilitating communication between sensors onboard the rover and the base-station computer. The rover featured several critical subsystems, including a wirelessly-controlled differential drive system, localization, navigation, and perception solutions, a science module, and a 6-DOF robotic arm. These subsystems shared information with the base-station computer over a long-range antenna using ROS 1 and ROS 2. Overall, we focused on implementing robust and reliable communication approaches to ensure low-latency operation, given the URC’s long-range, bandwidth-limited connection requirements.
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Real-Time Model-Based FTI integrity monitoring with AIFTI migrated from an architecture based on a PCM DAS to a networked distributed system, whose central element is now a Network File Server. FTI complexity and parameter count has increased significantly, encompassing the acquisition of a huge number of measurements. So, in several cases nobody knows whereas the parameter gathering is good or not. IPEV has successfully developed a Flight Test Simulator (FTS) to improve both test flight safety and test pilot and engineer formation syllabus. To improve aircraft model accuracy IPEV is now connecting into the Real-Time environment, the test bed, the Ground Telemetry System (GTS) and FTS, so parameter identification process and model tunning could be executed while the aircraft is flying. When the simulation model becomes accurate, we will be able to integrate an AI-based background process to monitor the integrity of parameters that are not observed by the ground crew. This paper discusses IPEV actual and future efforts to achieve such goal.
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WIDEBAND MICROPHONE ARRAY SYSTEM FOR SOUND LOCALIZATION AND SPECIES IDENTIFICATION IN WIND FARM ENVIRONMENTSWind energy facilities provide a sustainable power source but pose a significant threat to birds and bats that traverse these areas. To mitigate this risk, an acoustic monitoring system was designed to track and classify volant species. The system consists of an array of wideband microphones that enable time difference of arrival (TDOA) based 3D source localization using the generalized cross-correlation phase transform (GCC-PHAT) algorithm, accurate to within 1 degree in isolated environments. Furthermore, the microphones utilize a convolutional neural network (CNN) for species identification. The collected data maps migration patterns in the areas surrounding wind energy facilities, providing wind energy companies with actionable insights to adjust operations and reduce harm to wildlife.
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DEEP LEARNING-BASED MODULATION CLASSIFICATION USING SYNTHETIC AND OVER-THE-AIR SDR SIGNALSThis work presents a convolutional neural network (CNN) for automatic classification of dig ital modulation schemes using signals received via software-defined radios (SDRs). A synthetic dataset was created in MATLAB for BPSK, 8-PSK, 16-PSK, QAM, 16-QAM, and 64-QAM, with 1,500 messages per scheme at five SNR levels and added phase noise. A 12-layer CNN trained on this dataset achieved 97% accuracy in classifying modulation types. To evaluate real-world performance, over-the-air signals were captured and used for validation, yielding classification accuracies ranging from 72% to 91%. While performance on live signals showed variability, the results indicate strong potential for generalization with further refinement. Enhancing the synthetic dataset with additional channel impairments may improve model robustness and real-world appli cability. This research demonstrates the viability of using deep learning for signal classification in intelligent communication systems.
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SPECTRUM ANOMALIES ON SMALL SATELLITE LEO TO GEO RELAY LINKSWorking 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.
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UNSUPERVISED CLUSTERING OF ANOMALOUS SAMPLES OF TIME-SERIES DATA USING ANOMALY-DETECTION-CAPABLE STATISTICAL FEATURESDependable 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.
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ENHANCING CUBESAT TELEMETRY SYSTEMS FOR AUTONOMOUS SPACE MISSIONS UTILIZING MACHINE LEARNING TECHNIQUESA 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.
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Integrating Manned Control Systems and Camera Feeds for Aerial Vehicle ControlWe 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.
