Countering Small Unmanned Aircraft Systems with Advanced Data Analysis and Machine Learning is a Naval Post Graduate School Thesis by Robert Miske.
The DOD issued its inaugural Counter-Small Unmanned Aircraft Systems Strategy in January 2021 in response to the increasing threat posed by the rapid technological advancement and proliferation of sUAS to military personnel, facilities, and assets. Traditional counter-drone capabilities rely heavily on electronic warfare to disrupt the communication link between user and device and are no longer sufficient to address the evolving threat. This is because the threat now includes autonomous drones, COTS technology, and an increasing number of drones in the airspace that can overwhelm a C-sUAS operator. To address this increasingly complex small drone threat, the Army-led Joint Counter-sUAS Office is exploring materiel and non-materiel solutions for its new system-of-systems approach.
One of the significant challenges in C-sUAS is radar detection systems’ ability to differentiate between sUAS and other flying objects, such as birds. They are comparable in size, move slowly, and fly at low altitudes. An inaccurate or inefficient sUAS classification using radar data can pose a force protection threat due to the limited number of electro-optical sensors and human operators available for classification at scale.
This thesis aims to explore hidden structures in the bird and drone radar track data from two distinct training environments. It also aims to develop independent unsupervised and supervised learning models using the two datasets and experiment with data sampling and feature engineering to enhance model robustness to different environments and dynamic environmental conditions.
Publication Date- March 2023
Countering Small Unmanned Aircraft Systems with Advanced Data Analysis and Machine Learning contains the following major sections:
- Methodology and Models
- Application, Results, and Analysis
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Author- Robert Miske
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