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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Jianbo Rong, Yao Yang, Liu Liang, Zhifang Du, Xiaolin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming650504 China School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9781665498142

This paper deals with the adaptive classification problem of heavy-tailed data drawn from a multivariate distribution of the complex elliptically symmetric family. The authors assume that the collected data is either homogeneous or partially homogeneous. Based on the GLRT criterion, two adaptive classifiers are devised for the scenarios of Hermitian and persymmetric scatter matrices, respectively. To estimate the unknown parameters, iterative algorithms with convergence property are introduced to solve the developed non-convex maximum likelihood optimization problems. The simulation results show that, compared with the Hermitian scenario, the exploitation of persymmetric structure information can lead to an obvious performance improvement. © 2021 IEEE.

关键词： Classification GLRT heavy-tailed distribution majorization-minimization persymmetric structure

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Adaptive CFAR Detectors for Colocated MIMO Radar in Compound Gaussian Clutter

Adaptive CFAR Detectors for Colocated MIMO Radar in Compound...

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International Conference on Communication Technology (ICCT)

作者： Mengjiao Tang Yao Rong Sanfeng Hu Xiaolin Du Jianbo Li Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University China School of Computer and Control Engineering Yantai University China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China

This paper studies adaptive target detection in compound-Gaussian clutter for a colocated multi-input multi-output (MIMO) radar. A new one-step generalized likelihood ratio test (GLRT) is devised with all the involved unknown parameters estimated iteratively. Besides, a minor revision is made to the already existing two-step GLRT for this problem. Numerical examples show that both the two detectors are effective. For the considered simulation setup, the newly proposed detector can achieve higher detection rates than the revised one and competitors designed in Gaussian clutter scenarios.

关键词： Adaptation models MIMO radar Detectors Object detection Communications technology Numerical models Compounds

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Joint design of the transmit and receive beamforming via non-iterative optimization for MIMO radar

Joint design of the transmit and receive beamforming via non...

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IET International Radar Conference (IRC 2023)

作者： Yujia Li Xiaolin Du Mengjiao Tang Guolong Cui Yao Rong Computer and Control Engineering Yantai University Yantai People's Republic of China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming People's Republic of China Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China

ISBN: (数字)9781837240982

The joint design of the transmit and receive beamforming for multiple-input multiple-output (MIMO) radar systems is considered in the background of signal dependent interference. An optimization model using signal-to-interference-plus-noise-ratio (SINR) as a performance metric is designed under similarity cone, power, and energy constraints. An algorithm for simultaneous updating of transmit and receive (T-R) beamforming is presented, that bypasses the conventional T-R iteration and initialization, thus avoiding premature termination of the iteration or poor initialization. Simulation experiments verify the effectiveness of the method.

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A new estimator for the number of high-dimensional signals using ratio of higher-order moments

A new estimator for the number of high-dimensional signals u...

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IET International Radar Conference (IRC 2023)

作者： Lewen Zhang Yao Rong Mengjiao Tang Xiaolin Du Fan Li Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming People's Republic of China School of Computer and Control Engineering Yantai University Yantai People's Republic of China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan People's Republic of China

ISBN: (数字)9781837240982

This paper provides a novel estimation procedure for determining the number of high-dimensional complex-valued signals embedded in Gaussian white noise. Initially, this problem is formulated as a sequence of nested hypothesis tests. To enhance the decision performance, we introduce new test statistics based on the ratio of the fourth- and square second-order moments of the population covariance eigenvalues. This enables us to leverage higher-order moments information in the observed data, distinguishing it from conventional tests that rely solely on the first and second order moments. In the context of high-dimensional, complex-valued data and within a sub-sphericity testing framework, unbiased and consistent estimates are derived for these two higher-order moments. Subsequently, we conduct an asymptotic analysis to incoporate these estimates into the construction of the test statistics and the design of our proposed estimation scheme for determining the number of sources. Finally, numerical examples are presented to show its effectiveness and superiority over several classical estimation methods.

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Quasi-synchronization of chaotic systems with parameters mismatch via intermittent sampled-data control

Quasi-synchronization of chaotic systems with parameters mis...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ying Yang Ya-Li Zhi Zhi-Ming Zhang Qing-Zhi Wang Yunnan Key Laboratory of Statistical Modeling and Data Analysis School of Mathematics and Statistics Yunnan University Kunming China School of Electrical Engineering and Automation Anhui University Hefei China School of Mathematics and Statistics Fuyang Normal University Fuyang China Institute of Complexity Science School of Automation Qingdao University Qingdao China

In this paper, the problem of quasi-synchronization for chaotic systems with parameters mismatch is investigated. The periodically intermittent sampled-data controller is designed to realize the quasi-synchronization, which can reduce the control cost and the amount of data transmission. By constructing an augmented mixed-looped Lyapunov functional, the synchronization condition and the controller design condition are derived. Further, an expression to estimate the upper bound value of the norm for the error system state is obtained. An example is given to verify the proposed results.

关键词： Upper bound Costs Cyber-physical systems Trajectory Synchronization data communication

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The Gamma/Weibull Customer Lifetime Model

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Communications in Mathematics and Statistics 2019年第1期7卷 33-59页

作者： Gen Ye Songjian Wang Key Lab of Statistical Modeling and Data Analysis of Yunnan Province Yunnan UniversityKunming 650091People’s Republic of China

This paper proposes a new customer lifetime model:the Gamma/Weibull distribution(G/W).Similar to the Pareto/NBD model,we propose a G/W/NBD model by combining the G/W distribution with a negative binomial distribution(NBD)and study its properties such as(i)the probability that a customer to be alive at a time point;(ii)the expectation and variance of the number of transactions for a customer during a fixed time period;(iii)the conditional expectation and conditional variance of the number of future transactions for a customer during a fixed time *** simulation studies are conducted to investigate the forecasting accuracy and flexibility of the proposed model.A CDNOW data set is analyzed by the proposed model.

关键词： Customer lifetime Gamma distribution Negative binomial distribution Purchase history Weibull distribution

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Improved Far-field Speaker Recognition Method Based Geometry Acoustic Simulation and SpecAugment

Improved Far-field Speaker Recognition Method Based Geometry...

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2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Lian, Yingzi Pang, Jing Inner Mongolia University of Technology College of Science Hohhot China Inner Mongolia Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781665437301

Deep learning method has made a great breakthrough in the field of speaker recognition. However, the performance of deep learning method based neural network often degrade in far-field speaker recognition task, with respect to the near-field. One of the indispensable factors is that deep learning performance has a great impact on the quality and diversity of data. Utterance in far-field scenario always mixed with variant reverberation and noise which could interfere the prediction of model. In this paper, we proposed a novel method comprised of geometry acoustic simulation (GAS) and SpecAugment, which makes the performance of deep learning method on far-field speaker recognition task more stable and excellent. Based on near-field speech dataset, by means of physical simulation, far-field speech with different types and characters can be generated to provide as many scenes as possible for speaker recognition. Simultaneously, the SpecAugment method has been applied to enhance the diversity of utterance data on mel-spectrogram. Then, ResNet34 and AM-Softmax Loss were used as the most commonly used methods for feature extraction and model training. Our proposed method has been trained on Voxceleb1 and V oxceleb2 dataset, and test on Vox celeb original test set. Evaluations show that our proposed method has 46% and 40% of EER and minDCF relatively improvement on far-field speaker recognition task. © 2021 IEEE.

关键词： Deep learning

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Online federated learning framework for classification

arXiv

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arXiv 2025年

作者： Guo, Wenxing Xie, Jinhan Lu, Jianya Jiang, Bei Dai, Hongsheng Kong, Linglong School of Mathematics Statistics and Actuarial Science University of Essex ColchesterCO4 3SQ United Kingdom Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650091 China Department of Mathematical and Statistical Sciences University of Alberta EdmontonABT6G 2G1 Canada School of Mathematics Statistics and Physics Newcastle University Newcastle upon TyneNE1 7RU United Kingdom

In this paper, we develop a novel online federated learning framework for classification, designed to handle streaming data from multiple clients while ensuring data privacy and computational efficiency. Our method leverages the generalized distance-weighted discriminant technique, making it robust to both homogeneous and heterogeneous data distributions across clients. In particular, we develop a new optimization algorithm based on the Majorization-Minimization principle, integrated with a renewable estimation procedure, enabling efficient model updates without full retraining. We provide a theoretical guarantee for the convergence of our estimator, proving its consistency and asymptotic normality under standard regularity conditions. In addition, we establish that our method achieves Bayesian risk consistency, ensuring its reliability for classification tasks in federated environments. We further incorporate differential privacy mechanisms to enhance data security, protecting client information while maintaining model performance. Extensive numerical experiments on both simulated and real-world datasets demonstrate that our approach delivers high classification accuracy, significant computational efficiency gains, and substantial savings in data storage requirements compared to existing methods. © 2025, CC BY.

关键词： Federated learning

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Multi-band Emission up to PeV Energy from the Crab Nebula in a Spatially Dependent Lepto-hadronic Model

arXiv

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arXiv 2021年

作者： Peng, Qi-Yong Bao, Bi-Wen Lu, Fang-Wu Zhang, Li Department of Astronomy Key Laboratory of Astroparticle Physics of Yunnan Province Yunnan University Kunming650091 China Key Laboratory of Statistical Modeling and Data Analysis of Yunnan Province Yunnan University Kunming650091 China Department of Physics Yuxi Normal University Yuxi653100 China

Multi-band emission from radio to ultra-high energy gamma-rays in the Crab Nebula has been detected. To explain the observed results, non-thermal photon production in the Crab Nebula is carefully studied in a spatially dependent lepto-hadronic model. In our model, the dynamical evolution of the PWN is simulated in a spherically symmetric system. Both electrons and protons are accelerated at the termination shock. The relevant particle propagation equations as well as the photon evolving equation are simultaneously solved. For the Crab Nebula, our results reveal that the observed multi-band photon spectra can be well reproduced with reasonable model parameters. In particular, the photons with energy 200 TeV are mainly contributed by the hadronic component via proton-proton interaction. The contribution of the hadronic component depends on both proton spectral index αp and number density nH of medium within the PWN. Besides, high energy neutrino fluxes are predicted with variable proton spectral indices. The predicted fluxes are not only far below the sensitivities of current neutrino observatories, but also beneath the atmospheric neutrino background with energy less than ∼ 40 TeV. Moreover, the calculated radial profiles of surface brightness and spectral index are presented. © 2021, CC BY.

关键词： Photons

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data Classification Algorithms for Complex Heavy-Tailed Distributions

Data Classification Algorithms for Complex Heavy-Tailed Dist...

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IEEE International Conference on Radar

作者： Jianbo Li Yao Rong Liu Yang Zhifang Liang Xiaolin Du School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9781665468893

关键词： Maximum likelihood estimation Maximum likelihood detection Heavily-tailed distribution Symmetric matrices Simulation Radar detection Radar

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