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CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array

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Chinese Journal of Electronics 2019年第4期28卷 863-870页

作者： HAN Jun he minghao FENG Mingyue JIANG Ying Air Force Early Warning Academy

Constant false-alarm rate Block-sparse Bayesian learning(CFAR-BSBL) algorithm is proposed to reduce the computational complexity and improve Direction of arrival(DOA) estimation accuracy of offgrid signals with coprime array. Firstly, a signal model with normalized noise is built to avoid the learning procedure of noise parameter. Secondly, a block sparse Bayesian framework is built with the introduction of a temporary correlation matrix in order to use t he temporal structure of incident signals. Then the algorithm uses CFAR detection to detect the grids close to the real DOA and relieve the dependence on the number of signals. Finally,an off-grid process based on the closest grids is adopted to deal with the off-grid problem. The proposed CFAR-BSBL algorithm can obtain high accuracy and low complexity DOA estimation of off-grid signals with coprime array.

关键词： Off-grid direction-of-arrival estimation Block sparse Bayesian learning(BSBL) Coprime array CFAR detection Low complexity

2-D DOA Estimation Using Off-Grid Sparse Learning via Iterative Minimization with L-Parallel Coprime Array

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Chinese Journal of Electronics 2018年第6期27卷 1322-1328页

作者： FENG Mingyue he minghao HAN Jun CheN Changxiao Department of Postgraduate Management Air Force Early Warning Academy Air Force Early Warning Academy Radar Sergeant School Air Force Early Warning Academy Political Work Division Air Force Early Warning Academy

An L-parallel coprime array is designed and an Off-grid sparse learning via iterative minimization(OGSLIM) algorithm is proposed in order to improve the performance of Two-dimensional direction-of-arrival(2-D DOA) estimation. The L-parallel coprime array consists of two parts, one is a parallel coprime array and the other one is a linear coprime array perpendicular to the parallel coprime array. The OGSLIM algorithm is based on sparse Bayesian framework and can learn the off-grid parameter. Theory analysis and simulation results demonstrate that 2-D DOA estimation using OGSLIM algorithm with L-parallel coprime array can lead to higher estimation accuracy and resolution, it also fits to the underdetermined signals and correlated signals.

关键词： Two-dimensional direction-of-arrival(2 D DOA) estimation Off-grid problem L-parallel coprime array Off-grid sparse learning via iterative minimization(OGSLIM)

A Comprehensive Estimation Method for Kernel Function of Radar Signal Classifier

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Chinese Journal of Electronics 2015年第1期24卷 218-222页

作者： XU Jing he minghao HAN Jun CheN Changxiao Air Force Early Warning Academy

The current electromagnetism environment is fast changing and levity, the methods for evaluation Suppont vector machine(SVM) kernel functions which are used in radar signal recognition can not suit it. So kernel space separate, stability and parameter numbers were proposed in this paper to review the performance of kernel function, and a novel method for estimating kernel function was designed. By simulation, this novel method can estimate the performance of kernel function roundly, and can choose the best kernel function in different application demand for recognition.

关键词： Radar emitter Support vector machine(SVM) Kernel function Comprehensive estimation.

A New Method for Sorting Unknown Radar Emitter Signal

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Chinese Journal of Electronics 2014年第3期23卷 499-502页

作者： CheN Changxiao he minghao XU Jing HAN Jun Air Force Early Warning Academy

Sorting rate of common method is not high and it is sensitive to the Signal noise ratio(SNR), in order to solve these problems, bispectrum two dimensions characteristics complexity is applied to sort unknown complicated radar signal and a high sorting rate would be got. The bispectrum of received signal is extracted and it is predigested to two dimensions characteristic, the box dimension and information dimension are extracted from the two dimensions characteristic and used as the sorting characteristics, and the last sorting is accomplished by using KFCM algorithm. For the bispectrum of different signal is distinguishing and it is not sensitive to SNR, the box dimension and information dimension are divisible and steady, the advantage of this new method is validated by simulation results, and the lowest sorting rate is 90% at SNR equals to 10 dB.

关键词： Sorting Bispectrum Box dimension Information dimension SNR

A NOVEL METHOD FOR ESTIMATING The SNR OF UNKNOWN EMITTER SIGNAL

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Journal of Electronics(China) 2010年第1期27卷 134-138页

作者： Han Jun he minghao Tang Zhikai Hao Yuan Department of Information Countermeasures AFRA Wuhan 430010 China Division of Training AFRA Wuhan 430010 China

It is important to estimate the Signal-to-Noise Ratio(SNR) of unknown emitter signal *** order to resolve the disadvantages of present algorithm,a novel method is proposed in this *** extract and normalize the information of zero frequency of received signal by the Wigner-Vile Distribution(WVD) transformation and then get the approximate power of original signal by mathematic transformation,at last,we get the estimate value of SNR by the known account formula of *** results show that it is correct and feasible.

关键词： Signal-to-Noise Ratio(SNR) estimation Wigner-Vile Distribution(WVD) transformation Signal power

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Predicting the Bioaccessibility of Soil Cd,Pb,and As with Advanced Machine Learning for Continental-Scale Soil Environmental Criteria Determination in China

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Environment & health 2024年第9期2卷 631-641页

作者： Kunting Xie Jiajun Ou minghao he Weijie Peng Yong Yuan Guangdong Key Laboratory of Environmental Catalysis and health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangzhou 510006China health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China

Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with *** batch experimental methods and linear models,however,are time-consuming and often fall short in precisely quantifying *** this study,using 937 data points gathered from 56 journal articles,we developed machine learning models for three harmful inorganic elements,namely,Cd,Pb,and *** thorough analysis,the model optimized through a boosting ensemble strategy demonstrated the best performance,with an average R2 of 0.95 and an RMSE of *** further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence *** utilizing the developed integrated models,we carried out predictions for 3002 data points across China,clarifying the bioaccessibility of cadmium(Cd),lead(Pb),and arsenic(As)in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting(IDW)interpolation *** on these findings,we further derived the soil environmental standards for metallurgical sites in *** observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd,Pb,and As in mining/smelting sites from 5,58,and 14 to 1,24,and 7,*** research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.

关键词： machine learning soil environmental criteria potentially harmful elements bioaccessibility sites

DIGNN-A:Real-Time Network Intrusion Detection with Integrated Neural Networks Based on Dynamic Graph

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Computers, Materials & Continua 2025年第1期82卷 817-842页

作者： Jizhao Liu minghao Guo College of Computer Zhongyuan University of TechnologyZhengzhou450007China

The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security *** detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential ***,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between *** address these issues,this paper proposes a real-time network intrusion detection method based on graph neural *** proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured ***,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure ***,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection *** edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature *** efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this *** experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.

关键词： Intrusion detection graph neural networks attention mechanisms line graphs dynamic graph neural networks

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雷达辐射源信号识别特征参数集的构建方法

系统工程与电子技术 2018年第8期40卷 1729-1735页

作者：刘飞何明浩韩俊空军预警学院中国人民解放军95174部队

针对单个脉内特征在雷达辐射源信号(radar emitter signal,RES)识别中存在普适性差和识别性能"瓶颈"的问题,提出组合多个特征参与RES识别的方法。通过特征组合方式和识别性能的讨论分析,提出特征参数优型组合的概念,并在此基础上,研究... 详细信息

针对单个脉内特征在雷达辐射源信号(radar emitter signal,RES)识别中存在普适性差和识别性能"瓶颈"的问题,提出组合多个特征参与RES识别的方法。通过特征组合方式和识别性能的讨论分析,提出特征参数优型组合的概念,并在此基础上,研究特征参数集的构建方法。最后,根据对特征参数集的时间成本分析,提出两种典型的特征参数集——时效集和精准集,为不同需求场合的RES识别提供决策依据。

关键词：雷达辐射源脉内特征识别参数集时间成本

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A Theoretical Study of CdTe Flat-film Thickness Impact on AM1.5G Solar Absorption

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光学应用（中英文版） 2014年第1期3卷 1-5页

作者： minghao he Qijia Cheng Kezheng Li X.Y. Duan Hongyu Yu Department of Electrical and Electronic Engineering South University of Science and Technology of China 1088 TangchangRoad Shenzhen China