Countering Small Unmanned Aircrafts Systems With Advanced Data Analysis and Machine Learning
Abstract
In January 2021, the DOD released its first Counter-Small Unmanned Aircraft Systems Strategy to address the growing risk to military personnel, facilities, and assets posed by the rapid technological advancement and proliferation of sUAS. Existing counter-drone capabilities heavily reliant on electronic warfare to disrupt the communication link between user and device no longer address an evolving threat that includes autonomous drones, COTS technology, and an increasing number of drones in the airspace that can overwhelm a C-sUAS operator. To counter the increasingly complex small drone threat, the Army-led Joint Counter-sUAS Office is pursuing materiel and non-materiel solutions for its new system-of-systems approach. One vexing C-sUAS challenge involves radar detection systems discriminating some sUAS from other flying objects, like birds, due to their comparable size, slow movement, and low altitude. Inaccurate or inefficient sUAS classification using radar data can be a force protection threat due to the limited number of electro-optical sensors and human operators for classification at-scale. This thesis uses bird and drone radar track data from two different training environments to explore hidden structure in the data, develop independent unsupervised and supervised learning models using the two datasets, and experiment with data sampling and feature engineering to improve upon model robustness to different environments and dynamic environmental conditions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2023
- Accession Number
- AD1212947
Entities
People
- Robert Miske
Organizations
- Naval Postgraduate School