Review of micro‐Doppler signatures

Tahmoush, David

doi:10.1049/iet-rsn.2015.0118

Cited by 122 publications

(76 citation statements)

References 123 publications

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“…Micro-Doppler has been investigated for a variety of applications including search and rescue, security, law enforcement and defence [2][3][4], but the extraction of suitable information and features from the microDoppler signatures and the best methods to exploit these for classification, recognition, and identification are still current research fields [5].…”

Section: Introductionmentioning

confidence: 99%

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fioranelli

Ritchie

Gürbüz

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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Feature diversity for optimized human micro-doppler classification using multistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 53(2), pp. 640-654. (doi:10.1109/TAES.2017.2651678) This is the author's final accepted version.There may be differences between this version and the published version. FEATURE DIVERSITY FOR OPTIMIZED HUMAN MICRO-DOPPLER CLASSIFICATION USING MULTISTATIC RADARFrancesco Fioranelli (1) , Matthew Ritchie (2) , Sevgi Zübeyde Gürbüz (3) , Hugh Griffiths (2) ( AbstractThis paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes.

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Section: Introductionmentioning

confidence: 99%

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Fioranelli

Ritchie

Gürbüz

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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“…Accordingly, simple and low-cost sensors possibly arranged to form a sensor network become very attractive for many real-life applications. Besides surveillance and security, emerging fields which may benefit from the availability of such simple and low cost devices are ambient assisted living and elderly care, where radar signatures can be profitably exploited to recognize human gait [9][10][11][12][13][14] and monitor the movements of elderly subjects in smart homes with the aim to detect falls [15,16].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Through-Wall Situation Awareness Using a Microwave Doppler Radar Sensor

2016

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This paper deals with the development of a short-range radar suitable for the detection of humans behind visually opaque structures such as building walls. The system consists in a continuous wave Doppler radar operating in the S-band of the electromagnetic spectrum in order to ensure an adequate signal penetration through the walls. Based on the interaction of the electromagnetic waves with human targets, a phase modulation of the radar signal arises due to their movements and tiny periodic chest displacements associated with the respiratory activity. A simple and effective radar data processing algorithm is proposed to detect, in real-time, the presence of one or several human subjects in the through-wall scene. Such an algorithm automatically provides also an indication on whether the subjects are static or moving in the environment. As shown by experimental tests carried out in an indoor scenario, the proposed sensing device and related signal processing yields prompt and reliable information about the scene thus confirming its practical value.

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“…the additional Doppler frequency components on the human radar signature generated by movement of torso and swinging of limbs. These have been used for a variety of applications, including identification of people vs other targets such as vehicles and animals or of potentially armed personnel, and classification of specific individuals based on their walking gait [9][10]. Although the use of radar in the context of fall detection and indoor monitoring provided interesting preliminary results, challenges to be addressed remain.…”

Section: Introductionmentioning

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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Review of micro‐Doppler signatures

Cited by 122 publications

References 123 publications

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar

Real-Time Through-Wall Situation Awareness Using a Microwave Doppler Radar Sensor

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

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