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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jing Jian Huang Xiao Fa Zhang Lin Chen Chang Zhu Nai Chang Yuan State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense Technology Changsha China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

A 5×5 broadside slot array backed by a single cavity containing a mode filter is designed. The resonant slot length is obtained by using a waveguide slot model. The simulated and measured results show that this a...

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A Fast Deep-Learning Signal Detection Architecture for the Short-wave Band

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Journal of Physics: Conference Series 2020年第1期1646卷

作者： Keren Wang Weihao Li National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China. Information Engineering University Zhengzhou China

Wideband signal detection plays an important role in wireless communication systems. In recent years, deep learning (DL) has been introduced, and many trial efforts have been posed. In this paper, a fast deep-learning signal detection architecture is proposed for the short-wave band, which consists of the GPU-based Down Digital Converter (DDC), the DL-based signal detector, and the signal data viewer & recorder. Wideband signal data are taken directly as the input, and detected signals are automatically recorded as the output of the architecture. Experimental results suggest that our architecture is capable of detecting signals in the whole short-wave band in quasi-real-time, and specifically, with single GPU device.

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Direct Location for Multiple Passive Radars without and with Reference

Direct Location for Multiple Passive Radars without and with...

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2018 3rd International Conference on Communication,Image and Signal processing（CCISP 2018）

作者： K.K.Song Y.X.Yang H.F.Peng National Key Laboratory of Science and Technology on Blind Signal Processing

The problem of stationary target location by multiple passive radar sensors that using unknown and non-cooperative opportunity illuminator is considered. Traditional twostep approach which estimating the time difference of arrival(TDOA) and angle of arrival(AOA) firstly and locating using those parameters secondly. We explore the direct location with multiple passive radar sensors without estimating the intermediate parameters. As the reference path from the transmitter to receivers may be blocked in practice, we discuss the both cases that location without and with reference path. Two maximum likelihood algorithms of direct location are proposed for multiple passive radar sensors without and with reference. Monte Carlo simulations indicate that the direct location algorithm is superior to two-step approach with TDOA and AOA at low SNR for multiple passive radar sensors.

关键词： AOA Direct Location for Multiple Passive Radars without and with Reference

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Low Altitude, Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training

Low Altitude, Slow Speed and Small Size Object Detection Imp...

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2018 3rd International Conference on Communication,Image and Signal processing（CCISP 2018）

作者： J.F.Que H.F.Peng J.Y.Xiong National Key Laboratory of Science and Technology on Blind Signal Processing

The low altitude, slow speed and small size object which we call LSS-object for short, such as small UAV(unmanned aerial vehicles) have become a hot issue of air defense security, which is difficult to detect and identify accurately from the image. In this paper, aiming at the problem of LSS-object detection under noise environment, the detection method based on deep learning is proposed. Firstly, a standard training dataset consisting 5 classes of typical objects is constructed. Then, the standard dataset is augmented with noise of different intensity. Finally, YOLO v3 algorithm is used to form a LSS-object detection system which can adapt to environment noise. The training and detection experiments were carried out on the GPU server. After only using the noise-free dataset for training, the mAP(mean Average Precision) of the noise-free test set detection reached 81.07%, but the mAP decreased to 20.68% when the noise variance was *** adopting the mixed training strategy of the dataset with noise variance of 0.01 and noise-free data, the mAP for the test set detection with noise variance of 0.03 was increased to 70.61%, and the mAP still reached 79.85% in noise-free test set detection. The experiment results show that the mixed training strategy can greatly improve the accuracy in the noisy images detection while maintaining a higher accuracy in noise-free images.

关键词： image LSS Low Altitude Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training Object

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Passive packet reordering measurement on terrestrial-based and satellite-based internet 4th

Passive packet reordering measurement on terrestrial-based a...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Xu, Zhengguo Zheng, Hui National Key Laboratory of Science and Technology on Blind Signals Processing Chengdu610041 China

ISBN: (纸本)9789811039683

With the increasing deployment of various medium, the transmission performance has become a key issue of the Internet research. An important impact on the performance is the packet reordering, which is well-known in packet transmission. First, this paper provides a brief review of the existing packet reordering metrics. We also explore the flexibility of existing passive metrics. As a new rising transmission medium, the satellite-based Internet covers widespread areas nowadays, and its transmission performance is susceptible to the packet reordering. We carried out the experiments in practice network environment, and compare the results of the terrestrial-based and satellite-based Internet. Experimental results show the reordering ratio on satellite-based Internet is less than that of terrestrial-based Internet. Finally, we present a novel perspective to explain the reordering phenomenon in TCP/IP flows. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Transmission control protocol

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Network completion via deep metric learning

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Journal of Physics: Conference Series 2020年第1期1656卷

作者： Qiang Wei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China

Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small.

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An Online Updating Multi-Threshold Method for Mobility Relationship Mining 9

An Online Updating Multi-Threshold Method for Mobility Relat...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Zeng Chen Keren Wang Zhengguo Xu Jiaqi Yao National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781509038237;9781509038220

With the popularity of the mobile phones and location-based social networks, rich location data has become widely available nowadays, enabling study on friendship detection based on human mobility. However, in some circumstances, limited by data collection techniques, only discrete location（such as location IDs） can be fetched which leads to methods of detecting friendship based on distance metric is unrealistic. This paper aims to detect friendship among users based on their mobility data where locations are represented by location IDs. By considering each user’s preference for location and each location’s popularity, the difference between friends and strangers gets larger than using merely frequency of meeting events. However, we discover that measure values obtained may vary greatly among different users and propose to maintain a threshold for each user. In addition, an online updating algorithm is presented to update thresholds when a new user’s mobility data is available. Experiments conducted on MIT Reality Mining project dataset show that the proposed updating method is practical and the proposed multi-threshold model outperforms the state-of-theart methods proposed by Wang et al.

关键词： friendship detection multi-threshold mobility spatiotemporal online update

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Weighted Multidimensional Scaling Localization of a Known Altitude Object Using TOA Measurements

Weighted Multidimensional Scaling Localization of a Known Al...

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2018 International Conference on Computer, Electronic Information and Communications(CEIC 2018)

作者： Bing DENG Yu-cheng WENG Zheng-bo SUN National Key Laboratory of Science and Technology on Blind Signal Processing China Southern Airlines

This paper considers the problem of geolocating a target on the Earth surface whose altitude is known using the target signal time of arrival(TOA) measurements. The geolocation Cramer-Rao lower bound(CRLB) is derived and the performance improvement due to the availability of target altitude information is quantified. An weighted multidimensional scaling geolocation solution is developed. Its sensitivity to the target altitude error is also studied. Simulations verify the theoretical developments and illustrate the good performance of the proposed geolocation method.

关键词： Time of arrival Multidimensional scaling Target altitude

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Optimum Strategy of Reference Emitter Placement for Dual-Satellite TDOA and FDOA Localization

Optimum Strategy of Reference Emitter Placement for Dual-Sat...

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International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC)

作者： Kaiqiang Ren Zhengbo Sun National Key Laboratory of Science and Technology on Blind Signal Processing Southwest Institute of Electronic and Telecommunication Chengdu China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682470

An optimum strategy of reference emitter placement for dual-satellite time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization based on particle swarm optimization is proposed to improve localization accuracy. Firstly Cramer-Rao lower bound (CRLR) of dual-satellite TDOA and FDOA localization system with ephemeris error and system error is derived. Then, the optimum strategy of reference emitter placement is transformed to multidimensional constraint optimization problem by using CRLB as the criterion for performance of dual-satellite TDOA and FDOA localization system. Finally, optimum strategy of reference emitter placement is obtained by using particle swarm optimization (PSO) to solve the target function. Simulation results demonstrate that the proposed method can derive optimum strategy of reference emitter placement validly. Besides, when the optimum strategy of reference emitter placement is applied, only two reference emitters are demanded to eliminate the effect of ephemeris error and system error on localization accuracy.

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Low Altitude, Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training

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Journal of Physics: Conference Series 2019年第1期1169卷

作者： J. F. Que H. F. Peng J. Y. Xiong National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China.

The low altitude, slow speed and small size object which we call LSS-object for short, such as small UAV (unmanned aerial vehicles) have become a hot issue of air defense security, which is difficult to detect and identify accurately from the image. In this paper, aiming at the problem of LSS-object detection under noise environment, the detection method based on deep learning is proposed. Firstly, a standard training dataset consisting 5 classes of typical objects is constructed. Then, the standard dataset is augmented with noise of different intensity. Finally, YOLO v3 algorithm is used to form a LSS-object detection system which can adapt to environment noise. The training and detection experiments were carried out on the GPU server. After only using the noise-free dataset for training, the mAP(mean Average Precision) of the noise-free test set detection reached 81.07%, but the mAP decreased to 20.68% when the noise variance was *** adopting the mixed training strategy of the dataset with noise variance of 0.01 and noise-free data, the mAP for the test set detection with noise variance of 0.03 was increased to 70.61%, and the mAP still reached 79.85% in noise-free test set detection. The experiment results show that the mixed training strategy can greatly improve the accuracy in the noisy images detection while maintaining a higher accuracy in noise-free images.

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