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

Chen, Q; Liu, Y; Fioranelli, F; Ritchie, M; Tan, B; Chetty, K; (2019) DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets. IEEE Sensors Journal , 19 (11) pp. 4160-4172. 10.1109/JSEN.2019.2895538. Green open access

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Abstract

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 (DCNNs) termed single channel DopNet (SC-DopNet) and multiple channel DopNet (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 the aspect angle and the channel diversity in real-world scenarios. In addition, the 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 a data augmentation operation and a regularization term are proposed to train the SC-DopNet from scratch. Next, a factor analysis of the SC-DopNet is conducted based on various operating parameters in both the processing and radar operations. Third, to solve the problem of aspect angle diversity for the μ-DS classification, we design the MC-DopNet for multi-static μ-DS which is embedded with two new fusion schemes termed as greedy importance reweighting (GIR) and 121-Norm. These two schemes are based on two different strategies and have been evaluated experimentally. The GIR uses a win by sacrificing worst case approach, whereas 121-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 the MC-DopNet under different experimental scenarios.

Type: Article
Title: DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSEN.2019.2895538
Publisher version: https://doi.org/10.1109/JSEN.2019.2895538
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Legged locomotion, Feature extraction, Doppler radar, Training, Sensors, Doppler effect, DCNN, multi-static μ-DS, classification, armed personnel
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10065768
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