Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

TLDR

The study aims to detect and discriminate micro‑drones hovering or flying with varying payloads using multistatic micro‑Doppler signatures, assessing the added benefit over conventional radar. It extracts radar‑cross‑section, singular‑value‑decomposition, and centroid features from multistatic micro‑Doppler data to characterize drone payloads. Classification accuracy exceeded 96 % for hovering payload weights and surpassed 95 % for non‑hovering scenarios when distinguishing three payload levels.

Abstract

This study presents the use of micro‐Doppler signatures collected by a multistatic radar to detect and discriminate between micro‐drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the radar cross‐section of the micro‐drones, as well as the singular value decomposition and centroid of the micro‐Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid‐based features and multistatic information. For the non‐hovering scenarios, classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.