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

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 extrac...

Full description

Saved in:
Bibliographic Details
Published inIET radar, sonar & navigation Vol. 11; no. 1; pp. 116 - 124
Main Authors Ritchie, Matthew, Fioranelli, Francesco, Borrion, Hervé, Griffiths, Hugh
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 01.01.2017
Subjects
autonomous aerial vehicles
Classification
Cross sections
Doppler radar
Feature extraction
Hovering
microDoppler signatures
microdrones
multistatic information
multistatic microDoppler radar feature extraction
Navigation
Payloads
Radar
radar cross‐section
radar cross‐sections
radar detection
Radar signatures
Research Article
singular value decomposition
microdrones
microDoppler signatures
radar detection
autonomous aerial vehicles
feature extraction
radar cross-sections
Doppler radar
radar cross-section
singular value decomposition
multistatic microDoppler radar feature extraction
multistatic information
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2016.0063