Acoustic Network for UAS Detection, Tracking, and Classification
Abstract
Stevens Institute of Technology (SIT) is conducting research for the Air Force Research Laboratory (AFRL) for creating an expandable networked distributed counter UAS (CUAS) system. This system provides UAS detection, tracking, and classification based on passive acoustic sensing. A previous version, the Drone Acoustic Detection System (DADS) [1] used multiple microphone nodes, each equipped with a four-microphone array. The digitized microphone data is transferred over radio in realtime to a central computer, where it is processed. A new version, DADSv2 is under development and includes a larger number of nodes with a 10-microphone array. In the improved system, the initial stages of processing, detection, direction finding, and feature extraction are performed locally on a single-board computer built into the sensor node, while the localization and classification are performed on a central computer that processes data collected from all sensors. This significantly reduces the stream of data required for a centralized location, thus reducing the demands for radio performance. SIT has developed and tested signal processing methods for improving acoustic UAS detection, tracking and classification. These methods include spatial filtering (such as adaptive beamforming), various methods of ambient noise suppression, and the incorporation of beamforming for classification algorithms. Investigation of the acoustic array with seven microphones was conducted in a field test at ARFL tests facilities in NY and at a NJ local airport. The test demonstrated a significant increase in detection and classification distances using nodes with seven microphones and new methods of signal processing. SIT built the DADSv2 10 microphone node with digital microphones and onboard signal processing and is testing its performance. The new network with 8 similar nodes will be built in the nearest time.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 31, 2022
- Accession Number
- AD1195704
Entities
People
- Alexander Seduniv
- Alexander Sutin
- Daniel Kadyrov
- Darren Haddad
- Hady Salloum
- Nikolay Sedunov
Organizations
- Air Force Research Laboratory
- Stevens Institute of Technology