Low-Cost Raspberry-Pi-Based UAS Detection and Classification System Using Machine Learning release_rwx76pmcw5ab7hdiebeyf7m4ha

by Carolyn Swinney, John C. Woods

Published in Aerospace (Basel) by MDPI AG.

2022   Issue 12, p738

Abstract

Small Unmanned Aerial Systems (UAS) usage is undoubtedly increasing at a significant rate. However, alongside this expansion is a growing concern that dependable low-cost counter measures do not exist. To mitigate a threat in a restricted airspace, it must first be known that a threat is present. With airport disruption from malicious UASs occurring regularly, low-cost methods for early warning are essential. This paper considers a low-cost early warning system for UAS detection and classification consisting of a BladeRF software-defined radio (SDR), wideband antenna and a Raspberry Pi 4 producing an edge node with a cost of under USD 540. The experiments showed that the Raspberry Pi using TensorFlow is capable of running a CNN feature extractor and machine learning classifier as part of an early warning system for UASs. Inference times ranged from 15 to 28 s for two-class UAS detection and 18 to 28 s for UAS type classification, suggesting that for systems that require timely results the Raspberry Pi would be better suited to act as a repeater of the raw SDR data, enabling the processing to be carried out on a higher powered central control unit. However, an early warning system would likely fuse multiple sensors. These experiments showed the RF machine learning classifier capable of running on a low-cost Raspberry Pi 4, which produced overall accuracy for a two-class detection system at 100% and 90.9% for UAS type classification on the UASs tested. The contribution of this research is a starting point for the consideration of low-cost early warning systems for UAS classification using machine learning, an SDR and Raspberry Pi.
In application/xml+jats format

Archived Files and Locations

application/pdf  9.6 MB
file_gw76nv6psvhhtbvyr3p3fpoy6m
mdpi-res.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-11-22
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2226-4310
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: c5298ffb-3464-431d-87c9-a57a34755d0a
API URL: JSON