Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Ritchie, Matthew; Fioranelli, Francesco; Borrion, Hervé; Griffiths, Hugh
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 1(2017)
• Page Start: 116
• Page End: 124
• Publication Date: 2017-01-01
• Subjects: Doppler radar -- feature extraction -- radar cross‐sections -- singular value decomposition -- autonomous aerial vehicles -- radar detection; multistatic microDoppler radar feature extraction -- microdrones -- radar cross‐section -- singular value decomposition -- microDoppler signatures -- multistatic information; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0063
• Languages: English
• ISSNs: 1751-8784

Abstract

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.