Multi-time frequency analysis and classification of a micro-drone carrying payloads using multistatic radar

• Main Authors: Jarez S. Patel, Caesar Al-Ameri, Francesco Fioranelli, David Anderson
• Format: Article
• Language: English
• Published: Wiley 2019-08-01
• Series: The Journal of Engineering
• Subjects: learning (artificial intelligence); image classification; autonomous aerial vehicles; time-frequency analysis; convolutional neural nets; radar applications; alexnet; netrad; cnn; microdrone; pretrained convolutional neural network; domain transformations; prospective low altitude airspace security systems; normal operating conditions; open field; radar data; multidomain transformations; multistatic radar; multitime frequency analysis; drone hovering; six-class classification problem
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0551

This article presents an analysis of three multi-domain transformations applied to radar data of a micro-drone operating in an open field, with a payload (between 200 and 600 g) and without a payload. Inferring the presence of a drone attempting to transport a payload beyond its normal operating conditions is a key enabler in prospective low altitude airspace security systems. Two scenarios of operation were explored, the first with the drone hovering and the second with the drone flying. Both were accomplished through real experimental trials, undertaken with the multistatic radar, NetRAD. The images generated as a result of the domain transformations were fed into a pretrained convolutional neural network (CNN), known as AlexNet and were treated as a six-class classification problem. Very promising accuracies were obtained, with on average 95.1% for the case of the drone hovering and 96.6% for the case of the drone flying. The activations that these variety of images triggered within the CNN were then visualised to better understand the specific features that the network was learning and distinguishing between, in order to successfully achieve classification.