Cognitive passive radar system: software defined radio and deep learning approach

Wang, Qing; Du, Panfei; Dou, Tongdong; Gao, Lirong; Li, Chun

doi:10.1049/joe.2019.0628

Cited by 2 publications

(6 citation statements)

References 10 publications

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“…Researchers are focus on developing systems by using SDR based special hardware like USRP and image processing methods. Among lots of platforms, SDR based platforms famous for using capturing accurate WCSI data [16,17] [24][25][26][27], which motivated us to use. However, preparing an SDR platform is always challenging because hardware error and data error can show up at any moment.…”

Section: Motivationmentioning

confidence: 99%

“…Our main concern on image classification that why we focus on building a proper CNN model to achieving better accuracy results. In a few prior research works like [16,17] [ [24][25][26][27], SDR based hardware platform used to classify wireless signals using the CNN model. However, their designed CNN model is simple and not let them achieve high accuracy.…”

Section: Motivationmentioning

confidence: 99%

“…In Table 11, we presented prior works to make the comparison. Soltani et al [16] Wireless Signal CNN 94% Schmidt et al [17] Wireless Signal 1D-CNN 98% Wang et al [25] Wireless Signal CNN+LSTM 95% Liu et al [85] Wireless Signal CLDNN 88.5% Sedeghi et al [86] Radio Signal VT-CNN2 93% Kulin et al [87] Wireless Signal CNN 98%…”

Section: Comparisonmentioning

confidence: 99%

“…In [17], they used USRP N210 device with LabVIEW and used 1D CNN model to classify and identify signals. In [25], they used USRP N210 device with LabVIEW and used CNN and LSTM model together for classification and identification. In [85][86][87], they implement GNU Radio using USRP device for transmit and receive signals, and to classify signals they develop simple CNN model.…”

Section: Comparisonmentioning

confidence: 99%

“…The convolution network consists of shared weights and to achieve high translation invariance, only weighty features of the data use [23]. Prior research works like [24][25][26][27] used simple CNN model instead of complex model to classify wireless data. In this paper, we are using AlexNet and ZFNet, two renowned architecture of CNN in a parallel manner to classify data.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

et al. 2020

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In the past decades, cognitive computing and communication densely used in lots of networking areas. Current improvement in deep learning (DL) and big data analysis create great potential to analyze cognitive intelligence (CI) for many applications such as human activity monitoring and recognition through wireless communication. Cognitive intelligence and wireless communication are using to establish smart healthcare systems. Healthcare monitoring systems turn into interesting research subjects where monitoring post-operative surgical patients are the current focal point to the researcher. In this paper, we argue that deep learning along with the wireless communication technique introduces cognitive intelligence for the healthcare monitoring system. We present a deep learning based convolutional neural network (CNN) model to classify image data and a convenient and multi-functional software-defined radio (SDR) platform to detect movement of the ankle of patients who underwent ankle fracture surgery. Capturing wireless channel state information (WCSI) in the presence of the human body and classifying using CNN to observe distinct movements is the key idea of this study. A universal software radio peripheral (USRP) platform used to capture WCSI data and used for classification. AlexNet and ZFNet both are the famous architecture of CNN and used in a parallel way to classify captured WCSI-based images that converted from numeric data. The classification established on the ankles movements after surgery and classification results show that CNN provides satisfying results where test accuracy is 98.98%.

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