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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Wang, Xin Rong, Yao Tang, Mengjiao Li, Fan Hao, Chenchen Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming China University of China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight Guanghan China

ISBN: (纸本)9798331515669

This paper addresses adaptive subspace detection of range-distributed targets in compound Gaussian clutter with Generalized Inverse Gaussian (GIG) texture. Novel adaptive subspace detectors are developed based on the generalized likelihood ratio, Rao, and Wald test criteria, employing two-step design strategies that incorporate the persymmetric covariance structure and the GIG texture distribution to enhance detection performance. Furthermore, to achieve full adaptivity, a novel method is proposed for adaptively estimating the clutter texture parameters. The effectiveness of the proposed adaptive detectors, combined with this new texture parameter estimation method, is validated via numerical examples in various non-Gaussian clutter environments, demonstrating superior detection performance compared to several existing methods. © 2024 IEEE.

关键词： Clutter (information theory)

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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 2023, IRC 2023

作者： Li, Yujia Du, Xiaolin Tang, Mengjiao Cui, Guolong Rong, Yao Yantai University Yantai China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781839539954

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. © The Institution of Engineering & Technology 2023.

关键词： MIMO radar

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Adaptive Radar Detection in Compound-Gaussian Environments with Missing data 2

Adaptive Radar Detection in Compound-Gaussian Environments w...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Hao, Chenchen Rong, Yao Tang, Mengjiao Li, Fan Wang, Xin Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming China Civil Aviation Flight University of China Key Laboratory of Flight Techniques and Flight Safety Guanghan China

ISBN: (纸本)9798331515669

This paper addresses adaptive radar detection in scenarios with incomplete observations due to measurement errors, sensor failures, or outliers, where the target is embedded in compound Gaussian clutter with unknown covariance matrix. The detection problem is formulated as a binary hypothesis test, with a set of selection matrices introduced to represent the observations at each snapshot. At the stage of detector design, a two-step design strategy is employed: first, the Generalized Likelihood Ratio Test (GLRT) is derived assuming known covariance structure;then, the actual covariance matrix in the GLRT is replaced with its maximum likelihood estimate, which is calculated based on the non-missing components of the secondary data using an expectation-maximization (EM) algorithm developed in this paper. Numerical examples demonstrate the effectiveness of these detectors in compound Gaussian clutter environments, showing superior performance compared to the existing detector designed for scenarios with Gaussian interference and missing data, as well as the classical linear imputation method. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Robust Low-Rank Matrix Completion Under Generalized Gaussian Mixture Model With Application to DoA Estimation

Robust Low-Rank Matrix Completion Under Generalized Gaussian...

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IET International Radar Conference 2023, IRC 2023

作者： Tang, Mengjiao Zan, Zeqiong Rong, Yao Hu, Sanfeng Li, Fan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781839539954

This paper considers the problem of noisy low-rank matrix completion (LRMC), presenting a novel and robust LRMC approach that differs from classical methods reliant solely on Lp-norms for handling single noise distributions. Instead, we adopt a model that accounts for the noise using a Mixture of Generalized Gaussian (MoGG) distributions'in both the real and complex circularly symmetric cases'thus accommodating more intricate noise patterns. Our proposed method formulates the LRMC problem as the minimization of the negative log-likelihood coupled with a nuclear norm penalty, which is effectively addressed by the EM-ADMM algorithm devised in this paper. Moreover, the proposed complex-valued LRMC method is applied to tackle the Direction of Arrival (DoA) estimation with incomplete measurements. Specifically, we leverage the developed LRMC technique for imputation of the missing entries and then employ the MUSIC algorithm to estimate the angles of impinging signals. Finally, numerical examples are presented to show the performance improvement of our proposed method compared to some classical ones. © The Institution of Engineering & Technology 2023.

关键词： COMPLEX CIRCULARLY SYMMETRIC DISTRIBUTIONS DOA ESTIMATION LOW-RANK MATRIX COMPLETION MIXTURE OF GENERALIZED GAUSSIAN DISTRIBUTIONS

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Multi-view SAR Image Classification through Decision Fusion of Adaptive Dictionary Learning and CNN

Multi-view SAR Image Classification through Decision Fusion ...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Wang, Liyuqi Tang, Mengjiao Rong, Yao Ni, Meixin Li, Fan Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming China Civil Aviation Flight University of China Caac Laboratory of Flight Techniques and Flight Safety Guanghan China

ISBN: (纸本)9798350375909

This paper introduces a decision fusion strategy for automatic target recognition in multi-view synthetic aperture radar (SAR) images. Our proposed architecture integrates decisions derived from adaptive dictionary learning and Convolutional Neural Network (CNN) methods. Specifically, we employ two adaptive dictionary learning methods with distinct dictionary constructions to extract features from SAR images at each viewpoint, leading to intermediate decisions. Simultaneously, the CNN branch utilizes a multi-input single-output fully CNN to extract features from multiple viewpoints and make an intermediate decision. Subsequently, considering the multi-view images and leveraging the strengths of different methods, the classification results produced by these models are aggregated through voting to yield the final classification labels. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method, achieving an outstanding accuracy of 99.958% in a ten-class classification task. © 2024 IEEE.

关键词： Convolutional neural networks

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Rao Distance Based Spatial-Spectral Joint Feature Extraction and Semi-Supervised Classification for Hyperspectral Image 5

Rao Distance Based Spatial-Spectral Joint Feature Extraction...

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5th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2023

作者： Ni, Meixin Tang, Mengjiao Hu, Sanfeng Wang, Liyuqi Li, Fan Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan Kunming650504 China Civil Aviation Flight University of China CAAC Key Laboratory of Flight Techniques and Flight Safety Sichuan Guanghan618307 China

ISBN: (纸本)9798350329551

This paper presents a novel feature extraction approach for hyperspectral images, termed Spatial-Spectral Joint Principal Component analysis (PCA) coupled with Rao distance-based t-distributed Stochastic Neighbor Embedding (SSPCA-Rao-tSNE). Specifically, we apply the classical PCA method for spectral feature extraction while concurrently developing a Rao-distance-based tSNE algorithm to enhance the extraction of spatial features, particularly those with non-Euclidean structural characteristics. These spatial and spectral features are then combined through feature stacking to create spatial-spectral joint features. Furthermore, these spatial-spectral joint features are utilized in a semi-supervised graph convolutional network for hyperspectral image classification. Experimental results demonstrate the superior performance achieved by our newly proposed SSPCA-Rao-tSNE approach and the corresponding semi-supervised classification method in comparison to several classical methods. © 2023 IEEE.

关键词： Feature extraction

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Differentially private data release for mixed-type data via latent factor models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5682-5718页

作者： Yanqing Zhang Qi Xu Niansheng Tang Annie Qu Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Southwest United Graduate School Kunming China Department of Statistics University of California Irvine Irvine CA Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China

Differential privacy is a particular data privacy-preserving technology which enables synthetic data or statistical analysis results to be released with a minimum disclosure of private information from individual records. The tradeoff between privacy-preserving and utility guarantee is always a challenge for differential privacy technology, especially for synthetic data generation. In this paper, we propose a differentially private data synthesis algorithm for mixed-type data with correlation based on latent factor models. The proposed method can add a relatively small amount of noise to synthetic data under a given level of privacy protection while capturing correlation information. Moreover, the proposed algorithm can generate synthetic data preserving the same data type as mixed-type original data, which greatly improves the utility of synthetic data. The key idea of our method is to perturb the factor matrix and factor loading matrix to construct a synthetic data generation model, and to utilize link functions with privacy protection to ensure consistency of synthetic data type with original data. The proposed method can generate privacy-preserving synthetic data at low computation cost even when the original data is high-dimensional. In theory, we establish differentially private properties of the proposed method. Our numerical studies also demonstrate superb performance of the proposed method on the utility guarantee of the statistical analysis based on privacy-preserved synthetic data.

关键词： correlation factor model link function synthetic data

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Estimation of the Number of Signals in White Noise via Large-Sample and High-Dimensional Asymptotic Theory

Estimation of the Number of Signals in White Noise via Large...

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International Conference on Communication Technology (ICCT)

作者： Yao Rong Lewen Zhang Mengjiao Tang Zeqiong Zan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming Yunnan

This paper focuses on estimating the number of source signals embedded in Gaussian white noise. We address this problem via a sequence of nested hypothesis tests, and construct variance statistics based on eigenvalues of the sample covariance for each candidate hypothesis. Then, a detailed statistical analysis of these statistics is carried out in both the large-sample and high-dimensional regimes. According to this analysis, we propose a new scheme for determining the number of source signals in both the sample-rich and sample-starved cases. Finally, numerical examples are presented to show its superiority compared to some existing estimation methods.

关键词： statistical analysis Estimation White noise Eigenvalues and eigenfunctions Communications technology

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Adaptive CFAR Detectors for Colocated MIMO Radar in Compound Gaussian Clutter 22

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

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22nd IEEE International Conference on Communication Technology, ICCT 2022

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

ISBN: (纸本)9781665470674

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. © 2022 IEEE.

关键词： Clutter (information theory)

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Synchronization of delayed neural networks via intermittent sampled-data control

Synchronization of delayed neural networks via intermittent ...

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第43届中国控制会议

作者： Ying Yang Jifei Miao Xinyu Mei School of Mathematics and Statistics Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The paper investigates the synchronization problem for time-delay neural networks via intermittent sampled-data control. To fully consider the characteristics of the controlled systems, a mixed two-side-looped functional is constructed, and two inequalities are proposed to estimate the norm of the system state. After then, the synchronization criterion and the controller condition are obtained. To illustrate the effectiveness of the presented results, a numerical example is given.

关键词： Synchronization Neural networks Intermittent sampled-data control Time delay

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