Bistatic human micro-Doppler signatures for classification of indoor activities

Fioranelli, Francesco; Ritchie, Matthew; Griffiths, Hugh

doi:10.1109/radar.2017.7944276

Cited by 27 publications

(17 citation statements)

References 20 publications

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“…This system is more similar to a bistatic radar than to conventional WPS. The micro-Doppler shift caused by human activities can be further extracted from the channel state information (CSI) of the Wi-Fi, and analyzed for recognizing human actions [28], [29]. Potential applications of such techniques go far beyond the conventional indoor localization scenarios, which include health-care for elderly people, contextual awareness, anti-terrorism actions and Internet-of-Things (IoT) for smart homes [28], [30], [31].…”

Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

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1,164

602

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For the sake of enhancing the exploitation of the permanently allocated, but potentially under-utilized spectral resources, sharing the frequency bands between radar and communication systems has attracted substantial attention. More explicitly, there is increasing demand for sharing both the frequency band and the hardware platform between these two functionalities, but naturally, its success critically hinges on highquality joint sensing and communications. In this paper, we firstly overview the application scenarios and the research progress in the area of communication and radar spectrum sharing, with particular emphasis on: 1) Radar-communication coexistence; 2) Dual-functional radar-communication (DFRC) systems. In the remainder of the paper, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna aided user equipment (UE) operating in a mmWave channel, which in the meantime actively detects multiple targets. Note that part of the targets also play the role of scatterers for the communication signal. Given this framework, we then propose a novel scheme for joint target search and communication channel estimation relying on the omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design an HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the potential interference imposed on the UE signals by invoking the successive interference cancellation (SIC) technique, even when the radar and communication signals share the equivalent signal-to-noise ratio (SNR). The feasibility and the efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, our discussions are summarized by overviewing the open problems in the research field of CRSS.

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Section: ) Wi-fi Based Indoor Localization and Activity Recognitionmentioning

confidence: 99%

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Liu

Masouros

Petropulu

et al. 2020

IEEE Trans. Commun.

Self Cite

1,164

602

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“…with respect to their main Doppler component [1]. Target classification using micro-Doppler signatures has seen a rapid growth in recent years [2][3][4][5][6], with application in fields including surveillance [2] [3], healthcare [4] [5] and human-computer interaction [6]. Based on the human micro-Doppler signature, personnel recognition and human activity classification have attracted much attention [5,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Chen

Fioranelli

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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135

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Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, (doi:10.1109/LGRS.2018.2806940) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/156827/ Abstract-In this letter we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in our work to improve classification accuracy. We first propose the voted monostatic DCNN method (VMo-DCNN), which trains DCNNs on each receiver node separately, and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN method (Mul-DCNN), which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4 GHz multistatic radar system. Experimental results show that Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node.

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“…The regularization term can be shown in Eqn. (13), (14), where Logit j sof t is the output of j th channel after the Softmax operation, Logit j sof t [i] infers the probability output that the data from j th channel belongs to the i th class. Finally, the loss function using the 21 -Norm method is shown in Eqn.…”

Section: Single-channel(sc)-dopnetmentioning

confidence: 99%

“…In addition, µ-DS have been used to distinguish wind turbine blades and the blades of aircraft rotors [7]. It has also been demonstrated how µ-DS of different human target movements can help increase the situational awareness of the ambient assistant living in the healthcare context [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

et al. 2019

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The work presented in this paper aims to distinguish between armed or unarmed personnel using multi-static radar data and advanced Doppler processing. We propose two modified Deep Convolutional Neural Networks (DCNN) termed SC-DopNet and MC-DopNet for mono-static and multi-static micro-Doppler signature (µ-DS) classification. Differentiating armed and unarmed walking personnel is challenging due to the effect of aspect angle and channel diversity in real-world scenarios. In addition, DCNN easily overfits the relatively small-scale µ-DS dataset. To address these problems, the work carried out in this paper makes three key contributions: first, two effective schemes including data augmentation operation and a regularization term are proposed to train SC-DopNet from scratch. Next, a factor analysis of the SC-DopNet are conducted based on various operating parameters in both the processing and radar operations. Thirdly, to solve the problem of aspect angle diversity for µ-DS classification, we design MC-DopNet for multi-static µ-DS which is embedded with two new fusion schemes termed as Greedy Importance Reweighting (GIR) and 21-Norm. These two schemes are based on two different strategies and have been evaluated experimentally: GIR uses a "win by sacrificing worst case" whilst 21-Norm adopts a "win by sacrificing best case" approach. The SC-DopNet outperforms the non-deep methods by 12.5% in average and the proposed MC-DopNet with two fusion methods outperforms the conventional binary voting by 1.2% in average. Note that we also argue and discuss how to utilize the statistics of SC-DopNet results to infer the selection of fusion strategies for MC-DopNet under different experimental scenarios.

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Bistatic human micro-Doppler signatures for classification of indoor activities

Cited by 27 publications

References 20 publications

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

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