Dielectric Spectrum Feature Vector Extraction Algorithm of Ground Penetrating Radar Signal in Frequency Bands

Zhang, Hairu; Ouyang, Shan; Wang, Guofu; Wu, Suolu; Zhang, Faquan

doi:10.1109/lgrs.2014.2369523

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Nevertheless, the deep machine learning and soft computing methods have been used to classify the targets in different domain applications 1,2 . As a microwave imaging radar based on the stepped frequency continuous wave (SFCW) technique is commonly applied to various practical applications including civil engineering 3,4 , detection of pipes and cables buried in the ground 5,6 , through-wall radar imaging 7-11 , medical imaging 12 , feature estimation of the road surface layer 13 , and many applications of ground penetrating radar (GPR) [14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation.

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Section: Introductionmentioning

confidence: 99%

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

et al. 2021

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Development of a compact monocycle pulse generator for UWB impulse radar applications

Lee

Shaker

Melek

2020

Micro & Optical Tech Letters

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In this paper, a compact size (16 × 16 mm) of the monocycle pulse generator is proposed for Ultrawide‐band (UWB) impulse radar applications. The proposed circuit is based on the step recovery diodes (SRDs) and has a very simple structure. It consists of a coupling part, short pulse generating part, and monocycle forming part. The performance of the pulse generator is compared against simulation results which are performed by advanced design system (ADS). The measured monocycle waveform has a pulse width of 150 ps with the tail ringing level of −11 dB and symmetry of 87%. The corresponding spectrum has a center frequency of around 2.6 GHz with −10 dB bandwidth of 4 GHz. The performance of the pulse generator is validated by analyzing the receiving waveform. The pulse width and the ringing level of the received waveform are observed to be 250 ps and −11 dB, respectively. The developed compact monocycle pulse generator is very attractive because of the ease of integration into the limited physical space inside the UWB system.

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Real-time pedestrian detection method based on improved YOLOv3

Luo

Wang

2020

J. Phys.: Conf. Ser.

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from VOC data set and train with INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2 and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

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Dielectric Spectrum Feature Vector Extraction Algorithm of Ground Penetrating Radar Signal in Frequency Bands

Cited by 8 publications

References 11 publications

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

Development of a compact monocycle pulse generator for UWB impulse radar applications

Real-time pedestrian detection method based on improved YOLOv3

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