Radar-Based Classification of Unmanned Aerial Vehicles (UAVs) Carrying Payloads
H. Visvanathan Sethuraman (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Olexander Yarovyi – Mentor (TU Delft - Microwave Sensing, Signals & Systems)
F. Fioranelli – Mentor (TU Delft - Microwave Sensing, Signals & Systems)
R. T. Rajan – Graduation committee member (TU Delft - Signal Processing Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Unmanned Aerial Vehicles (UAVs), commonly referred to as drones have gained increasing popularity with current technological breakthroughs. Recent reports indicate the number of registered drones in the United States have crossed 850,000 and is expected to increase multi-fold over the coming years. The widespread applications of drones include agriculture, transportation, mining, media, entertainment, etc. While drones are used for many benevolent purposes, there are also multiple real-life incidents, where drones have caused serious mishaps. Radars with high resolution are increasingly used for drone detection and classification, thanks to their long-range, all-weather monitoring capabilities. Several techniques for binary classification of drone vs no drone, drone vs birds, and different models of drones have been proposed based on the relevant features extracted from the micro-Doppler signatures or from tracks’ information. Recently, research focusing on the problem of classifying drone(s) carrying payloads has garnered considerable attention.
In this thesis, the ability of a fully polarimetric radar and a single polarimetric radar to discriminate between payloads carried by UAVs is demonstrated. A novel approach has been employed in the feature extraction algorithm, where features from individual and combined polarimetric channels are extracted for classification. Decision and ensemble fusions on the respective extracted features proved to enhance the classification performance. The robustness of the algorithm is validated on two experimental radar datasets acquired in the scenarios where the UAVs carrying payloads of different weights are hovering, flying back and forth, and flying along rectangular waypoints. Initial results for the fusion techniques provide approximately 95%-99% classification accuracy for the polarimetric and statistical features.