Article
Engineering, Electrical & Electronic
Feng Lin, Weiyu Li, Qing Ling
Summary: This paper addresses the problem of distributed learning under Byzantine attacks and proposes a Byzantine-robust stochastic ADMM method. The effectiveness of the proposed method is demonstrated through theoretical analysis and numerical experiments.
Article
Automation & Control Systems
Ting Bai, Shaoyuan Li, Yuanyuan Zou
Summary: This paper proposes a novel distributed model predictive control scheme based on ADMM to address the control problem of linear systems with changeable network topology. The scheme enables quick response when modifying network topology while maintaining satisfactory dynamic performance.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Mathematics, Applied
Fengmiao Bian, Jingwei Liang, Xiaoqun Zhang
Summary: The paper proposes combining ADMM with a class of variance-reduced stochastic gradient estimators for solving large-scale non-convex and non-smooth optimization problems. Global convergence is established under the additional assumption that the object function satisfies Kurdyka-Lojasiewicz property, and numerical experiments are conducted to demonstrate the performance of the proposed methods.
Article
Energy & Fuels
Yang Hu, Meng Zhang, Kaiyan Wang, DeYi Wang
Summary: This paper proposes a method for solving the optimization problem of wind power and large-scale EVs synergistic access to the power grid based on a distributed framework. By establishing a rolling real-time optimization model and employing an improved ADMM approach, the parallel optimization of each agent is achieved. The effectiveness and feasibility of the method are verified through example simulation.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Civil
Jun Ma, Zilong Cheng, Xiaoxue Zhang, Masayoshi Tomizuka, Tong Heng Lee
Summary: In this paper, a method using iterative linear quadratic regulator (iLQR) and alternating direction method of multipliers (ADMM) for motion planning in the context of autonomous driving is proposed. The method achieves high computation efficiency under various constraints and enables real-time computation and implementation, providing additional safety to on-road driving tasks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Chemistry, Physical
Xiaodong Wei, Chao Sun, Qiang Ren, Feikun Zhou, Weiwei Huo, Fengchun Sun
Summary: This paper proposes a method based on the ADMM algorithm to solve the energy management problem for fuel cell vehicles, demonstrating faster computation speed and higher accuracy through simulation results.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Computer Science, Information Systems
Chengwen He, Yunbin Yuan, Bingfeng Tan
Summary: This study considers NLOS errors as outliers and transforms the TOA-based localization problem into a sparse optimization one in LOS-dominating environment. Sparse technology is introduced into LOS/NLOS scenarios, formulating a L1-norm minimization problem, and solved using ADMM algorithm with iterative adaptive strategy. The method demonstrates advantages of high computation speed and positioning accuracy under mixed sparse LOS/NLOS scenarios based on Monte Carlo simulation results.
Article
Engineering, Electrical & Electronic
Qin Liu, Ning Chu, Liang Yu, Yue Ning, Peng Wu
Summary: This article proposes an efficient acoustic localization method for low-frequency sound source localization. The method achieves non-synchronous measurement at coprime positions and solves the corresponding inverse problem using the alternating direction method of multipliers (ADMM) algorithm. By moving and constructing a virtual array, the aperture and synthetic aperture of the array are enlarged, leading to increased spatial resolution. Simulations and experiments are conducted to verify the efficiency and robustness of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Operations Research & Management Science
Sedi Bartz, Ruben Campoy, Hung M. Phan
Summary: This paper proposes and studies an adaptive version of ADMM for the case where the objective function is the sum of a strongly convex function and a weakly convex function. By combining generalized notions of convexity and penalty parameters with the convexity constants of the functions, we prove convergence of the algorithm under natural assumptions.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2022)
Article
Engineering, Aerospace
Wenxia Wang, Shefeng Yan, Linlin Mao, Xiangyu Guo
Summary: This study investigates adaptive beamforming with sidelobe-level control in the presence of signal steering vector uncertainty. The proposed iterative optimization algorithms using the ADMM framework successfully handle uncertainty set and sidelobe constraints with low computational complexity. Theoretical analyses and simulations confirm the performance advantages of the algorithms in low sample support, steering vector mismatch, and real-time snapshot update scenarios.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Article
Mathematics, Applied
Jianchao Bai, Yuxue Ma, Hao Sun, Miao Zhang
Summary: This paper investigates a convex optimization problem with multi-block variables and separable structures. A partial LQP-based ADMM algorithm is proposed, and the convergence and convergence rate are analyzed using a prediction-correction approach.
APPLIED NUMERICAL MATHEMATICS
(2021)
Article
Engineering, Electrical & Electronic
Zekun Liu, Siwei Yu
Summary: This paper proposes an ADMM-based optimization algorithm to solve the MMV problem. The key innovation is the introduction of an l(2,0)-norm sparsity constraint, which differs from the widely used l(2,1)-norm constraint. The proposed algorithm is shown to solve a larger range of MMV problems even under adverse conditions, as demonstrated by comparisons with other algorithms using simulated examples.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho Sow, Qing Ling
Summary: This paper investigates the problem of minimizing a sum of convex composite functions over a decentralized network. The proposed penalty ADMM method shows sublinear convergence for convex private functions and linear convergence when the smooth parts are strongly convex. Numerical results confirm the theoretical analyses and demonstrate the advantages of PAD over existing state-of-the-art algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Mathematics
Zhangquan Wang, Shanshan Huo, Xinlong Xiong, Ke Wang, Banteng Liu
Summary: This paper proposes an adaptive parameter selection method based on the ADMM, which decomposes a convex model-fitting problem into a set of sub-problems that can be executed in parallel. The effectiveness of the algorithm is verified through experiments on eight classification datasets, showing improved speed of data processing and increased parallelism.
Article
Engineering, Electrical & Electronic
Ekin Nurbas, Emrah Onat, T. Engin Tuncer
Summary: The paper introduces a new method for DoA estimation in distributed sensor array networks using ADMM and SBL framework, showing improved performance in local arrays and reduction in transmitted parameters. Experimental results demonstrate that distributed use of ADMM efficiently enhances DoA estimation performance.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Houhong Xiang, Baixiao Chen, Minglei Yang, Saiqin Xu, Zhengjie Li
Summary: This paper proposes an improved DOA estimation method based on LSTM neural networks, which can enhance accuracy and generalization capability in the presence of array imperfections.
APPLIED INTELLIGENCE
(2021)
Article
Engineering, Electrical & Electronic
Zhaoming Zhang, Baixiao Chen, Minglei Yang
Summary: This article investigates a TR detection algorithm for a multistatic radar system in a variable multipath channel environment, proposing a TR-LRT detector to utilize the characteristics of multiple paths. Through Monte Carlo simulations and theoretical analysis, the superior performance of the proposed TR detector compared to a conventional one is demonstrated, showing good robustness to environmental variations. Simulation results indicate better detection results are achieved in more severe multipath scattering environments.
IET RADAR SONAR AND NAVIGATION
(2021)
Article
Engineering, Electrical & Electronic
Saiqin Xu, Baixiao Chen, Houhong Xiang
Summary: Tracking low-angle targets over an uneven surface is challenging due to the presence of highly correlated, complicated, and volatile multipath signals encountered in radar. An effective direction-of-arrival (DOA) estimation approach based on maximum likelihood (ML) estimator, known as optimal synthetic vector maximum likelihood (OSVML) method, has been proposed to address this challenge. The performance of this method has been evaluated through simulations and field data sets acquired from S-band radar, showing promising results in naval environments.
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS
(2021)
Article
Engineering, Aerospace
Zhaoming Zhang, Baixiao Chen, Minglei Yang
Summary: The study proposes a novel method based on time reversal for detecting the performance of moving targets in a multipath environment. Results show that time reversal method can increase detection probability and improve detection performance by exploiting multipath effects.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Zhaoming Zhang, Baixiao Chen, Minglei Yang, Meng Liu
Summary: This study combines polarization diversity and time-reversal technology to propose a time-reversal detector suitable for a polarization array radar, aiming to enhance target detection performance. Numerical simulations demonstrate the superiority of the time-reversal detector and elaborate on the impact of channel variations on detection performance.
IET RADAR SONAR AND NAVIGATION
(2022)
Article
Engineering, Electrical & Electronic
Shuai Peng, Baixiao Chen, Minglei Yang
Summary: The paper formulates the coherent DOA estimation problem as a joint-sparse recovery problem by exploiting signal sparsity in the spatial domain. A method based on CS theory and the generalized MUSIC criterion is proposed, which improves on MUSIC and achieves reliable support recovery under rank-defect conditions. The proposed method achieves super-resolution by modifying the criterion and is shown to have satisfactory performance even in the case of coherent sources.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Husheng Wang, Baixiao Chen, Dongchen Zhu, Fengsheng Huang, Xiangzhen Yu, Qingzhi Ye, Xiancheng Cheng, Shuai Peng, Jiaqiu Jing
Summary: This study proposes a method for identifying chaff that integrates the distribution of distance, Doppler frequency, and power. The method effectively identifies chaff using multiple detectors and analyzes the theoretical detection probability and influencing factors.
IET RADAR SONAR AND NAVIGATION
(2022)
Article
Engineering, Electrical & Electronic
Dongchen Zhu, Xiancheng Cheng, Baixiao Chen
Summary: This article introduces a scheme that combines electromagnetic vector sensor arrays with compression networks to reduce hardware cost and algorithm complexity. A compressed reduced dimensional multiple signal classification algorithm is derived based on the signal model to effectively reduce computational complexity. An optimization method based on signal-to-noise ratio criterion is proposed to address the information loss problem in coefficient matrix selection.
IET RADAR SONAR AND NAVIGATION
(2022)
Article
Engineering, Electrical & Electronic
Dongchen Zhu, Baixiao Chen, Xiancheng Cheng
Summary: Electromagnetic vector sensors (EMVS) are widely used in array signal processing due to their polarization diversity advantages, but they come with a large hardware cost and computational complexity. To address this, we propose a novel compression framework that reduces hardware cost while achieving high performance. By using a sparse network of analog phase shifters and a two-step multi-parameter estimation algorithm, we are able to obtain high-accuracy estimates with reduced front-end chains and low computational complexity.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Theory & Methods
Saiqin Xu, Baixiao Chen, Houhong Xiang
Summary: Tracking low-elevation targets over an uneven surface is difficult due to complicated and volatile multipath signals. Machine learning-based methods, which do not rely on prior assumptions, can adapt better to array imperfections. This paper proposes a DOA estimation approach that combines the Sum/Difference pattern with a deep neural network to fully learn the features of the direct signal. Simulation experiments and field data sets verify the computational superiority and practicality of the proposed method in severe multipath effects.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Xiaoying Chen, Baixiao Chen
Summary: Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent interference. The classical ISRJ model has constraints on forwarding strategies. We generalize the classical ISRJ model and propose an iterative decomposition method based on it. Simulation experiments show that the proposed method has excellent recognition and suppression performance for different types of ISRJ.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Saiqin Xu, Alessandro Brighente, Baixiao Chen, Mauro Conti, Xiancheng Cheng, Dongchen Zhu
Summary: Received signal Direction of Arrival (DOA) estimation is a significant problem with wide-ranging applications. Current approaches struggle to separate closely located transmitters without using a large number of antennas, resulting in higher costs. In this paper, we propose a deep learning framework that can estimate DOA under Line-of-Sight scenarios, distinguishing more closely located sources than the number of receiver's antennas. Our approach reduces hardware complexity compared to state-of-the-art solutions and performs well in demanding scenarios with low SNR and limited snapshots.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Husheng Wang, Baixiao Chen, Qingzhi Ye
Summary: This article investigates anti-jamming strategies and establishes a cognitive radar antagonistic environment model to solve the problem of anti-jamming decision-making using reinforcement learning algorithms. The feasibility of applying reinforcement learning in radar antagonistic environments is also verified.
IET RADAR SONAR AND NAVIGATION
(2023)
Article
Engineering, Electrical & Electronic
Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod K. Varshney
Summary: In this work, a joint collaboration-compression framework is proposed for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). The framework involves collaboration between local sensors via a collaboration matrix and linear compression of observations before transmission. Near-optimal collaboration and compression strategies are designed under power constraints, and the methods can also be used for estimating time-varying random vector parameters.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Xiancheng Cheng, Baocheng Geng, Prashant Khanduri, Baixiao Chen, Pramod K. Varshney
Summary: This work presents a joint collaboration-compression framework for random signal detection in a resource constrained wireless sensor network. The framework involves local sensor collaboration, linear compression of information, and the introduction of a novel metric for evaluating detection performance. The proposed strategies are optimized under power constraints through the use of the generalized deflection coefficient metric.
IEEE SIGNAL PROCESSING LETTERS
(2021)
