Xuegang Wang

University of Electronic Science and Technology of China

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Xuelian Yu

University of Electronic Science and Technology of China

Lin Zou

University of Electronic Science and Technology of China

Yun Zhou

University of Electronic Science and Technology of China

Haohao Ren

University of Electronic Science and Technology of China

Zhuming Chen

Guangdong Medical College

Zaojian Zou

Shanghai Jiao Tong University

Zhongzhi Li

Fudan University

Chaoshu Jiang

University of Electronic Science and Technology of China

Youxin Lu

University of Electronic Science and Technology of China

Qun Wan

University of Electronic Science and Technology of China

Cooperative Institutions

University of Electronic Science and Technology of China

109

Chinese Academy of Sciences

Shanghai Jiao Tong University

Beijing Institute of Technology

University of Chinese Academy of Sciences

University of Science and Technology of China

Peking University

Jilin University

Tsinghua University

Xi'an Jiaotong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

2-D DOAs estimation in impulsive noise environments using joint diagonalization fractional lower-order spatio-temporal matrices

Science in China Series F Information Sciences (2008)

Tieqi Xia Qun Wan Xuegang Wang Yi Zheng

Matrix (chemical analysis)

Narrowband

Uncorrelated

10.1007/s11432-008-0082-5

Cite

Citations (3)

Development of wideband digital array radar

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (2013)

Tao Li Xuegang Wang

As the further development of narrowband digital array radar, wideband digital array radar(WDAR) get more and more research all over the world, for it not only has the advantages of narrowband digital array radar such as compact structure, large dynamic range and flexible digital beam control ability, but also can do high resolution processing such as target imaging. In this paper, based on studying some existing experimental platforms of WDAR, the authors present the basic architecture and function modules of WDAR, analyze the characteristics of WDAR compared with conventional radar, and then some key technologies of WDAR are concisely discussed, the last section conclude the paper.

Narrowband

Wideband

Fire-control radar

3D radar

10.1109/iccwamtip.2013.6716650

Cite

Citations (4)

Methods of eliminating Doppler dispersion in synthetic wideband signal

International Conference on Microwave and Millimeter Wave Technology (2008)

Wei Peng Xuegang Wang Jian-hong Zhao

As the technological advances, it is possible to perform wide bandwidth signal processing for high range resolution and target identification in the modern radar systems. In order to prevent the deleterious effects of dispersion for wideband signals, a wideband signal can be synthesized by coherently combining the individual narrow band components, meanwhile, clutter mitigation, estimation and discrimination of target's velocity is enabled by performing coherent pulse train in each subband. However, the separate and independent processing using conventional Doppler filter bank in a subband can also introduce deleterious effects of target Doppler dispersion among subbands and cause the distortion on synthesized one-dimensional range profile. In this paper, we first analyze the target Doppler dispersion among subbands and deleterious effects of following recombination, and then propose an effective method to eliminate the deleterious effects of target Doppler dispersion in subbands. The Method is easy to be realized and tractable and do not require an increment of computation. Its good performance is verified by simulation results.

Wideband

SIGNAL (programming language)

10.1109/icmmt.2008.4540743

Cite

Citations (5)

An ultra-low sidelobe pulse-compression filter

2014 IEEE REGION 10 SYMPOSIUM (2014)

Minglei Cui Lin Zou Xuegang Wang

Pulse compression is widely used in modern radar as signal processing approach to enable long-range detection and good resolution to be achieved simultaneously. The successful application of pulse compression techniques depends largely on reduction of the range sidelobes. Aimed at range sidelobes reduction of the LFM pulse compression result, a new weighting pulse-compression filter is presented, based on the spectrum modification technique. The design and implementation of this filter is exhibited in detail. Subsequent performance analysis shows that after filtered by the proposed filter, the best Ratio of Mainlobe to Sidelobe (RMS) of output short pulse is below -75dB. Furthermore, the RMS of compression output with different Doppler shift is presented to prove the proposed filter can be applied in most radar applications.

Pulse compression

Matched filter

10.1109/tenconspring.2014.6863092

Cite

Citations (1)

Deep Neural Network based Classification of Rolling Element Bearings and Health Degradation Through Comprehensive Vibration Signal Analysis

Journal of Systems Engineering and Electronics (2022)

Delanyo Kwame Bensah Kulevome Hong Wang Xuegang Wang

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.

Rolling-element bearing

Leverage (statistics)

10.23919/jsee.2022.000023

Cite

Citations (11)

Radar target recognition based on multiple features fusion with Dempster-Shafer theory

Yun Zhou Xuelian Yu Minglei Cui Xuegang Wang

A radar high resolution range profile (HRRP) contains rather detailed structural information of a target and provides us a more reliable tool for target recognition. Various feature extraction methods have been developed and applied to radar target recognition successfully. Usually, different features contain different discriminative information. So the recognition performance will be improved by utilizing different features extracted from HRRP together. In this paper, a radar target recognition scheme based on multiple features fusion is presented. Firstly, four different features are obtained by using PCA, DCT, LDA and SNPE respectively. Then a simple nearest neighbor classifier (NNC) or nearest neighbor line classifier (NNLC) is employed for classification with each individual feature. Finally, based on the Dempster-Shafer evidential theory, the recognition results with each individual feature are fused efficiently to get a better recognition performance. Experimental results on measured profiles from three aircrafts indicate the effectiveness of the presented method.

Discriminative model

Dempster–Shafer theory

Automatic Target Recognition

10.1109/icemi.2011.6037723

Cite

Citations (11)

A Bayesian Approach to Active Self-Paced Deep Learning for SAR Automatic Target Recognition

IEEE Geoscience and Remote Sensing Letters (2020)

Haohao Ren Xuelian Yu Lorenzo Bruzzone Yukun Zhang Lin Zou

Deep learning has attracted intensive attention in synthetic aperture radar (SAR) automatic target recognition (ATR). Usually, a considerable number of labeled samples are necessary to learn a deep model for obtaining good generalization capability. However, the process of sample labeling is time-consuming and costly. This letter proposes an active self-paced deep learning (ASPDL) approach to SAR ATR. In a nutshell, we first introduce the Bayesian inference into the process of deep model parameter optimization, aiming at learning a robust classification model in the case of a limited number of labeled samples. Next, a cost-effective sample selection strategy is presented to iteratively and actively select the informative samples from a pool of unlabeled samples for labeling. Concretely, high-confidence samples are actively selected through self-paced learning (SPL) way and automatically pseudo-labeled with the current classification model, whereas low-confidence samples are chosen through an active learning strategy and manually labeled. Finally, we update the parameters of the model by minimizing a dual-loss function using a new training set that is constructed by incorporating new labeled samples with original ones. Experiments on the moving and stationary target acquisition and recognition (MSTAR) benchmark data demonstrate that the proposed method can achieve better classification accuracy with relatively few labeled samples compared with some state-of-the-art methods.

Automatic Target Recognition

Target acquisition

Benchmark (surveying)

Sample (material)

10.1109/lgrs.2020.3036585

Cite

Citations (23)

High Resolution Range Profile Recognition Using Robust Kernel Neighborhood Preserving Projection

Yun Zhou Xuelian Yu Minglei Cui Xuegang Wang Zhongzhi Li

A new manifold learning algorithm, called robust kernel neighborhood preserving projection (RKNPP), is presented and applied to radar target recognition based on high resolution range profiles. RKNPP attempts to map the high-dimensional data into such a low-dimensional space where points belonging to the same class are close to each other while points belonging to different classes are far away from each other, while preserving the main geometric structure of the original data. A sophisticated distance metric is utilized to construct the neighborhood graph of the input data, which has several good properties that are helpful to limit the effect of noise, and thus make RKNPP a robust classification method for real-world data. Moreover, in RKNPP, a simple technique of eigenvalue decomposition is applied to deal with the small sample size problem, to which not much attention has been paid in many manifold learning algorithms. Experimental results on measured data demonstrate the promising performance of the proposed method.

Kernel (algebra)

Manifold (fluid mechanics)

Data point

10.1109/cit.2012.165

Cite

Citations (0)

Narrow Band Interference Suppression of Loran-c Signal

Gnss World of China (2011)

Xuegang Wang

Narrow band interference(NBI) is a kind of interference which exists in loran-c signal which receiver receives.NBI often conceals loran-c signal,so it is very important for receivers' working normally.It adopts LMS adaptive algorithm and a daptive notch filter with 2-taps to process loran-c signal within NBI.From the simulation we could know that,LMS adaptive algorithm has an good effect on carrier wave interference and near carrier wave interference,but its implementing needs many taps and it is difficult to obtain expection signal because of coding information,Loran-c signal resulting is realizing difficultly.Two-tapped adaptive notch filter algorithm completely wipes off interference point,so it cann't be used to suppress carrier wave interference.However,it has a good effect on near-carrier wave interference and none-carrier wave interference,and its realization is very easy and is easy to used in project.

SIGNAL (programming language)

Source

Cite

Citations (0)

Effect of shot blasting on Aerial Aluminum Conductor corrosion behavior

Kai Guo Xiaoming Wang Zhiling Yu Xingeng Li Zengwu Yue

Aerial aluminum conductor (ACSR) as the primary overhead lines products, has been running in the field for a long time.The inevitable corrosion will be affected by heavy pollution environment.Salt spray is acknowledged as the natural environment in most demanding of corrosion, which can make the wire obviously brittle and the strength greatly decreased.The resulting in ballooning and broken strands of the wire break will seriously affect the safe and reliable operation of power grid.Steel core aluminum stranded wires with shot blasting on their main body status are hard to shake in the current and future decades, and the shot blasting of ACSR is effective way to improve its corrosion resistance for great significance.

10.2991/icmmcce-15.2015.510

Cite

Citations (0)