Acoustic Drone Detection Sensor Network
Faculty Mentor Name
Seth McNeill, Luis Felipe Zapata-Rivera, Catalina Aranzazu-Suescun
Format Preference
Poster
Abstract
The proliferation of drones has created a challenge for places like borders, where drones are being used for smuggling, and airports, where drones infringe on controlled airspace . Finding as many ways to detect drones as possible is important for border and airport security. Typical ways to detect drones are to listen for the RF signature of a control signal or to use optical means of recognizing drones in images. However, for situations like smuggling, a drone can be run completely autonomously, leaving no RF control signal to listen for, and at night without lights or in fog the optical methods also fail. Therefore, a different modality for detecting the drones is needed. We propose to use sound (acoustics) to detect drones traveling nearby. Acoustic detection is not meant to replace RF and optical methods, but instead to add to the suite of drone detection capabilities. We expect to be able to detect drones out to at least fifty feet even with background aircraft noise using small, low-cost microcontrollers and microphones. Our sensors will be both recording their data locally and reporting it live to Amazon Web Services Internet of Things (AWS loT) where it can be monitored in real time. This project entails not only acoustic detection, but the creation of a reporting infrastructure using long distance LoRa radios bridged to Wi Fi and sent to AWS loT with end-to-end encryption. We will also develop weather resistant, solar powered nodes.
Acoustic Drone Detection Sensor Network
The proliferation of drones has created a challenge for places like borders, where drones are being used for smuggling, and airports, where drones infringe on controlled airspace . Finding as many ways to detect drones as possible is important for border and airport security. Typical ways to detect drones are to listen for the RF signature of a control signal or to use optical means of recognizing drones in images. However, for situations like smuggling, a drone can be run completely autonomously, leaving no RF control signal to listen for, and at night without lights or in fog the optical methods also fail. Therefore, a different modality for detecting the drones is needed. We propose to use sound (acoustics) to detect drones traveling nearby. Acoustic detection is not meant to replace RF and optical methods, but instead to add to the suite of drone detection capabilities. We expect to be able to detect drones out to at least fifty feet even with background aircraft noise using small, low-cost microcontrollers and microphones. Our sensors will be both recording their data locally and reporting it live to Amazon Web Services Internet of Things (AWS loT) where it can be monitored in real time. This project entails not only acoustic detection, but the creation of a reporting infrastructure using long distance LoRa radios bridged to Wi Fi and sent to AWS loT with end-to-end encryption. We will also develop weather resistant, solar powered nodes.
