Exploitation of multipath micro‐Doppler signatures for drone classification. Issue 4 (1st April 2020)

• Record Type: Journal Article
• Authors: Zhang, Pengfei; Li, Gang; Huo, Chaoying; Yin, Hongcheng
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 4(2020)
• Page Start: 586
• Page End: 592
• Publication Date: 2020-04-01
• Subjects: time‐frequency analysis -- principal component analysis -- Fourier transforms -- Doppler radar -- remotely operated vehicles -- robot vision -- image classification -- radar resolution -- radar imaging -- image fusion; drone classification -- microdrone classification -- high‐resolution radar -- multipath echoes -- multipath features -- multipath spectrograms -- multipath exploitation -- direct‐path echo -- microDoppler radar signature; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0105
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In this study, the authors present a method of fusion of direct‐path and multipath micro‐Doppler radar signatures to improve the accuracy of micro‐drone classification in urban environments. By using a high‐resolution radar, the direct‐path and multipath echoes are separated based on the indices of the range cells they occupy. The time–frequency spectrograms of the drones are obtained by performing the short‐time Fourier transform on the direct‐path and multipath echoes, respectively. The direct‐path and multipath features, which are extracted by the principal components analysis on the direct‐path and multipath spectrograms, respectively, are fused and fed into classifiers to determine the type of drones. Experimental results based on measured data demonstrate that the classification accuracy produced by multipath exploitation is 5% higher than that obtained by using the direct‐path echo only.