Radar‐ID: human identification based on radar micro‐Doppler signatures using deep convolutional neural networks

Cao, Peng; Xia, Wei; Ye, Ming; Zhang, Jutong; Zhou, Jianjiang

doi:10.1049/iet-rsn.2017.0511

Cited by 94 publications

(54 citation statements)

References 20 publications

(24 reference statements)

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“…Vandersmissen et al [5] utilized a low-power FMCW radar for indoor person identification based on gait characteristics. Cao et al [19] applied radar MD signatures for person identification with a deep CNN.…”

Section: Person Identificationmentioning

confidence: 99%

“…To show the advantage of combining radar-based activity recognition and person identification tasks together with the MTL mechanism, we compare the performance of MRA-Net for the two tasks with that of several state-of-the-art methods. At present, the deep convolutional neural networks (DCNNs) in [5,19] are two typical models for radar-based person identification, and we selected them as baselines. The result is shown in Figure 10a.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

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A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Jing

2019

Remote Sensing

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Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted.

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Section: Person Identificationmentioning

confidence: 99%

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Jing

2019

Remote Sensing

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“…During the last two years, deep convolutional neural network (DCNN) has been successfully applied to image recognition [8][9][10][11][12]. Compared with other traditional recognition algorithms, DCNN does not rely on handcrafted features.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with other traditional recognition algorithms, DCNN does not rely on handcrafted features. There are also a number of researches about recognition of body gestures reported based on MDSs using DCNN [8][9][10][11][12]. In [8], four targets, i.e., human, dog, horse, and car, were measured for human detection, and seven body gestures were measured for human activity recognition.…”

Section: Introductionmentioning

confidence: 99%

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Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network

Kang¹,

Zhang²,

Dong³

2019

PIER M

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Body gesture recognition can be applied not only to social security but also to rescue operations. In reality, body gesture can produce unique micro-Doppler signatures (MDSs), which can be used for identification. In this paper, we first acquired the echo signals of four body gestures via a Ka-band dual-polarization radar system under different angles and distances. The four gestures are respectively swinging arm up and down, swinging arm left and right, nodding, and shaking head. Then, time-frequency spectrograms were obtained by short-time Fourier transform, from which we can see that different body gestures have different polarimetric MDSs. Finally, we propose to classify four body gestures using the deep convolutional neural network (DCNN) method. It is shown that by combining HH and HV polarizations, about 92.7% recognition rate is achieved while only about 77.5% and 89.3% rates are obtained by using single HH polarization and single HV polarization, respectively.

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Person identification with limited training data using radar micro‐Doppler signatures

Lang

Wang

Yang

et al. 2019

Micro & Optical Tech Letters

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Radar‐based human micro‐Doppler analysis has been the subject of much investigation in recent years. Apart from the conventional activity classification task, person identification based on human movement signal has emerged as a research interest. This paper presents a method to recognize a person's identity from varied human motions using an ultra‐wideband radar. The human movement data is captured in an indoor environment and is then transformed into micro‐Doppler spectrograms for identification. Moreover, as it is always challenging to construct large scale radar datasets in practice, we adopt a plain convolutional neural network with a multi‐scale feature aggregation strategy to address the identification problem. Experimental results show that the micro‐Doppler signatures have great potential in person identification, and our model presents relative satisfying performances limited training set. Especially, when “walking” is used for identification, our approach achieves a person identification accuracy of 96.8% for the four targets used.

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Radar‐ID: human identification based on radar micro‐Doppler signatures using deep convolutional neural networks

Cited by 94 publications

References 20 publications

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network

Person identification with limited training data using radar micro‐Doppler signatures

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