This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distributions. Existing truncation methods either assume hard constraints, or use numerical methods to handle uncertain constraints. The proposed approach introduces moment-based Gaussian approximations of the truncated distribution. This method can be applied to numerous problems, with the motivating problem being Kalman filtering with uncertain constraints. In a simulation example, the developed method is shown to outperform unconstrained Kalman filtering by over 40% and hard-constrained Kalman filtering by over 17%.
Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression
Zhejiang University（浙江大学）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；National Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）
COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression
Technical University of Munich（慕尼黑工业大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tongji University（同济大学）；Shanghai Yangzhi Rehabilitation Hospital（上海市养志康复医院）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong Polytechnic University（香港理工大学）
Linear-Quadratic Gaussian Games with Distributed Sparse Estimation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学学院）；DEVCOM Army Research Laboratory（美军研究开发司令部陆军研究实验室）
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Technical University of Munich（慕尼黑工业大学）；Robert Koch Institute（罗伯特·科赫研究所）；Freie Universität Berlin（柏林自由大学）；The Hong Kong University of Science and Technology（香港科技大学）
Vectorized Gaussian Belief Propagation for Near Real-Time Fully-Distributed PMU-Based State Estimation
Faculty of Electrical Engineering, University of Sarajevo（萨拉热窝大学电气工程学院）；Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University（亚琛工业大学E.ON能源研究中心复杂电力系统自动化研究所）；Fraunhofer FIT（弗劳恩霍夫FIT研究所）；Faculty of Technical Sciences, University of Novi Sad（诺维萨德大学技术科学学院）
