Smarter Signals: Predicting the SNR Threshold for Accurate Direction-of-Arrival Detection in UAV Communication Systems
Disciplines
Aeronautical Vehicles | Digital Communications and Networking | Navigation, Guidance, Control, and Dynamics | Other Mechanical Engineering | Power and Energy | Robotics | Signal Processing | Systems and Communications | Systems Engineering and Multidisciplinary Design Optimization
Abstract (300 words maximum)
Reliable communication between unmanned aerial vehicles (UAVs) or drones and ground stations is crucial to next-generation autonomous systems. However, Direction-of-Arrival (DoA) estimation accuracy, the identification of where a signal is coming from, is strongly dependent on the signal-to-noise ratio (SNR). This project develops a predictive model to identify the "transition SNR," the point at which DoA algorithms switch between unreliable and reliable detection. Using MATLAB simulations and Random Matrix Theory (RMT), we model the evolution of the Mean-Squared Error of angle estimates under noise and confirm those predictions through hardware experiments using LoRa radios, USRP platforms, and Raspberry Pi-based UAV nodes. Beyond signal detection, this work explores RF energy harvesting, scavenging ambient radio signals to power small systems, as a path towards drone flight time extension or powering remotes. By integrating DoA estimation with power-saving communication protocols, we aim to develop smarter UAV-to-ground links that provide more effective use of limited energy resources. This multi-disciplinary project integrates signal processing, embedded systems, and power engineering to improve the efficiency of wireless communications in challenging environments. Our research explains when and why DoA algorithms break down, paving the way for more adaptive and sustainable UAV networks.
Use of AI Disclaimer
no
Academic department under which the project should be listed
SPCEET – Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Adeel Khalid
Smarter Signals: Predicting the SNR Threshold for Accurate Direction-of-Arrival Detection in UAV Communication Systems
Reliable communication between unmanned aerial vehicles (UAVs) or drones and ground stations is crucial to next-generation autonomous systems. However, Direction-of-Arrival (DoA) estimation accuracy, the identification of where a signal is coming from, is strongly dependent on the signal-to-noise ratio (SNR). This project develops a predictive model to identify the "transition SNR," the point at which DoA algorithms switch between unreliable and reliable detection. Using MATLAB simulations and Random Matrix Theory (RMT), we model the evolution of the Mean-Squared Error of angle estimates under noise and confirm those predictions through hardware experiments using LoRa radios, USRP platforms, and Raspberry Pi-based UAV nodes. Beyond signal detection, this work explores RF energy harvesting, scavenging ambient radio signals to power small systems, as a path towards drone flight time extension or powering remotes. By integrating DoA estimation with power-saving communication protocols, we aim to develop smarter UAV-to-ground links that provide more effective use of limited energy resources. This multi-disciplinary project integrates signal processing, embedded systems, and power engineering to improve the efficiency of wireless communications in challenging environments. Our research explains when and why DoA algorithms break down, paving the way for more adaptive and sustainable UAV networks.