This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information from neighbors. A distributed sparse least squares algorithm is proposed by minimizing a local information criterion formulated as a linear combination of accumulative local estimation error and L_1-regularization term. The upper bounds of the estimation error and the regret of the adaptive predictor of the proposed algorithm are presented. Furthermore, by designing a suitable adaptive weighting coefficient based on the local observation data, the set convergence of zero elements with a finite number of observations is obtained under a cooperative non-persistent excitation condition. It is shown that the proposed distributed algorithm can work well in a cooperative way even though none of the individual sensors can fulfill the estimation task. Our theoretical results are obtained without relying on the independency assumptions of regression signals that have been commonly used in the existing literature. Thus, our results are expected to be applied to stochastic feedback systems. Finally, the numerical simulations are provided to demonstrate the effectiveness of our theoretical results.
Online Parameter Identification of Generalized Non-cooperative Game
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海自主智能无人系统科学中心）
Identification and Adaptation with Binary-Valued Observations under Non-Persistent Excitation Condition
Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统与控制重点实验室）；School of Mathematical Science, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
Department of Mechanical and Aerospace Engineering, University of Florida（机械与航空航天工程系，佛罗里达大学）；Munitions Directorate, Air Force Research Laboratory（空军研究实验室弹药部门）
Recursive Binary Identification under Data Tampering and Non-Persistent Excitation with Application to Emission Control
The Hong Kong Polytechnic University（香港理工大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongyuan University of Technology（中原工学院）
Observer-Based Estimation and Hydrostatic Inertia Modeling for Cooperative Transport of Variable-Inertia Loads with Quadrotors
Norwegian University of Science and Technology（挪威科技大学）；Universidad de San Andrés (UdeSA)（圣安德烈斯大学）；CONICET（阿根廷国家科学研究委员会）；Centre for Automation and Robotics (CSIC-UPM)（自动化与机器人中心）
Auto-Optimization with Active Learning in Uncertain Environment: A Predictive Control Approach
Hubei Normal University（湖北师范大学）；Loughborough University（拉夫堡大学）；The University of Manchester（曼彻斯特大学）；Hong Kong Polytechnic University（香港理工大学）；Southeast University（东南大学）
$\ell_1$-Based Adaptive Identification under Quantized Observations with Applications
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
An almost globally convergent observer for visual SLAM without persistent excitation
Australian Centre for Field Robotics & Sydney Institute for Robotics and Intelligent Systems, The University of Sydney（悉尼大学澳大利亚野外机器人中心与悉尼机器人与智能系统研究所）；DJI Innovation Inc.（大疆创新科技有限公司）
Lossy Cooperative UAV Relaying Networks: Outage Probability Analysis and Location Optimization
School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；Research institute of Information Fusion, Naval Aviation University（海军航空大学信息融合研究所）；University of Houston（休斯顿大学）；IMT-Atlantic（大西洋电信学院）；University of Oulu（奥卢大学）
State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, College of Engineering, Peking University（湍流与复杂系统国家重点实验室，力学与工程科学系，工程学院，北京大学）；Massachusetts Institute of Technology（麻省理工学院）；Queen’s University（皇后大学）；KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering, University of Cyprus（科斯卓越研究中心与创新中心，塞浦路斯大学电气与计算机工程系）
