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

Ritchie, Matthew; Fioranelli, Francesco; Borrion, Hervé; Griffiths, Hugh

doi:10.1049/iet-rsn.2016.0063

Cited by 123 publications

(74 citation statements)

References 17 publications

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“… Features based on Singular Value Decomposition (SVD), in particular the mean and the standard deviation of the first three vectors of the left (U) and right matrix (V) resulting from the decomposition. These have been previously used for classification of unarmed vs potentially armed personnel and for micro-drones' payloads classification [17,19].…”

Section: Data Analysis and Preliminary Resultsmentioning

confidence: 99%

Feature diversity for fall detection and human indoor activities classification using radar systems

Shrestha

Kernec

Fioranelli

et al. 2017

International Conference on Radar Systems (Radar 2017)

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This paper presents preliminary analysis of radar signatures for fall detection and classification of human indoor activities, to monitor the daily behaviour of individuals at risk of deteriorating physical or cognitive health. Two datasets of signatures in different environments have been collected, one of which included signatures generated from signals simultaneously collected from a radar and an RGB-D Kinect sensor, on a couple of older individuals. This preliminary analysis shows the potential effectiveness of different features and classifiers, and highlights the need of additional investigation to characterise and exploit the diversity of features and classification methods, in different experimental scenarios with different subjects.

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Section: Data Analysis and Preliminary Resultsmentioning

confidence: 99%

Feature diversity for fall detection and human indoor activities classification using radar systems

Shrestha

Kernec

Fioranelli

et al. 2017

International Conference on Radar Systems (Radar 2017)

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“…ISAR imaging of space targets with moving parts usually produces micro-Doppler phenomena, which reduce the image quality of ISAR and interfere with the resolution of the target [8,9]. In this paper, we introduce an ISAR imaging algorithm which can effectively remove the micro-Doppler effect.…”

Section: Isar Imaging Algorithm For Space Targets With Rotating Partsmentioning

confidence: 99%

Research on micro-Doppler feature of spatial target

Zhang

et al. 2017

J Wireless Com Network

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The micro-Doppler (m-D) effect which is also called micro-motion effect is a kind of physical phenomenon, which is often produced in ISAR imaging of space targets. When the space target has a micro-Doppler effect, it will produce frequency modulation for the radar echo and cause serious interference to ISAR imaging of the target. In this paper, the micro-spin model is modeled, and the echo of micro-spin model scattering point is processed respectively by short-time Fourier transform (STFT), Wigner-Vile Distribution (WVD), and pseudo-Wigner-Vile Distribution (PWV), and the micro-Doppler curve is derived. On this basis, for the space target with spin components, this paper introduces an ISAR imaging algorithm using the micro-Doppler removal based on STFT and proposes an improved algorithm based on rigid body parameter estimation, which can obtain better imaging results.

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“…When a target is illuminated by an electromagnetic wave, its fine motions produce micro‐Doppler signatures. Several researchers have investigated these signatures and their use in classification . Joint time‐frequency analyses have been applied to time‐domain Doppler signals to observe the time history of Doppler frequencies.…”

Section: Introductionmentioning

confidence: 99%

Extraction of micro‐doppler characteristics of drones using high‐resolution time‐frequency transforms

Kim

Nazaroff

2018

Micro & Optical Tech Letters

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The demand for detecting and tracking drones has increased for reasons of surveillance and security. Radar is one of the promising methods in this regard. The recognition and identification of drones using a radar system requires the extraction of their unique micro‐Doppler signatures produced by their rotating blades. Because of the blades' rapid rotation speed, difficulties are inherent in visualizing clear micro‐Doppler signatures in a conventional joint time‐frequency analysis such as the short‐time Fourier transform. In this paper, we propose the use of high‐resolution transform techniques to visualize the micro‐Doppler signatures of drones in a spectrogram. The techniques used include Wigner‐Ville distribution, smoothed pseudo‐Wigner‐Ville distribution, and short‐time MUltiple SIgnal Classification (MUSIC) algorithm. In particular, the latter, which had never previously been applied to drones, is suggested to visualize the details of micro‐Doppler signatures. We measured three drones using a continuous‐wave radar, and performances of these algorithms were compared using data collected from the drones. We could observe that the short‐time MUSIC method showed the clearest spectrogram for identifying micro‐Doppler signatures. This study can potentially be useful in the field of drone classification.

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

Cited by 123 publications

References 17 publications

Feature diversity for fall detection and human indoor activities classification using radar systems

Feature diversity for fall detection and human indoor activities classification using radar systems

Research on micro-Doppler feature of spatial target

Extraction of micro‐doppler characteristics of drones using high‐resolution time‐frequency transforms

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