Article
Mathematics, Applied
Michael Herty, Elisa Iacomini
Summary: This study applies and adapts the ensemble Kalman Filter method and a weighted function approach to solve coupled inverse nonlinear problems. The analysis of the mean field limit of the ensemble method leads to an explicit update formula for the weights. Numerical examples demonstrate the improved performance of the proposed method.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Juan P. Cortes, Gabriel A. Alzamendi, Alejandro J. Weinstein, Juan I. Yuz, Victor M. Espinoza, Daryush D. Mehta, Robert E. Hillman, Matias Zanartu
Summary: Subglottal Impedance-Based Inverse Filtering (IBIF) allows for the continuous estimation of glottal airflow. However, long-term monitoring may result in deviations due to various factors. To address this, a Kalman filter implementation of the IBIF filter is proposed to estimate model uncertainty and correct for signal deviations.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart
Summary: This paper focuses on the optimization approach to solve inverse problems using stochastic dynamical systems and techniques from nonlinear Kalman filtering. The extended Kalman filter, ensemble Kalman filters, and unscented Kalman inversion are applied in the study. The paper presents a novel stochastic dynamical system and shows improved inversion results compared to previous work.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Environmental Sciences
Pei Ye, Meng-Dao Xing, Xiang-Gen Xia, Guang-Cai Sun, Yachao Li, Yuexin Gao
Summary: The paper introduces a new method for reconstructing high-resolution ISAR images using Kalman filtering, which corrects the state vector through a two-step KF process of prediction and update to achieve a well-focused high-resolution image in a short observation time. The proposed method demonstrates good performance in both simulated and real data, addressing the conflict between short observation time and high resolution requirements.
Article
Engineering, Civil
Qing Zeng, Xiaoyang Hu, Xiaodong Shi, Yiting Ren, Yuan Li, Zhongdong Duan
Summary: This study proposes a Kalman Filter-based scheme to indirectly evaluate road roughness using measurements of tire pressure. The scheme uses Extended Kalman Filter to calibrate parameters and solves unknowns in the vehicle's state-space equation using Discrete Kalman Filter. The results demonstrate the reliability of the proposed scheme and reveal the potential for better estimation of road roughness at lower running speeds.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2022)
Article
Mathematics, Applied
Felix G. Jones, Gideon Simpson
Summary: In this article, the possibility of using classical filtering methods to solve linear statistical inverse problems is discussed. The authors propose optimizing the regularization parameter in the filters to reduce the mean squared error. Building on previous work, the authors prove that considering the problem in a weaker norm and applying iterate averaging can lead to convergence of 3DVAR in mean square, regardless of the choice of parameter. It is also shown that iterate averaging does not improve the performance of the Kalman filter in this setting.
NUMERICAL ALGORITHMS
(2023)
Article
Chemistry, Analytical
Arkadiusz Czarnuch, Marek Stembalski, Tomasz Szydlowski, Damian Batory
Summary: This study verifies the effectiveness of a road simulator by testing the signal response of a multi-axle vehicle passing an obstacle. The results show a high correlation between the simulation and real test results. Additionally, using a numerical model confirms the accuracy of the input signal generated by the road simulator.
Article
Chemistry, Analytical
Jin Han, Jia Liu, Hongmei Chang
Summary: This paper introduces a high-speed non-contact vehicle-mounted road undulation elevation detection method, which improves the detection accuracy by combining the advantages of different sensors, and compares the performance of different detection methods.
Article
Automation & Control Systems
Kwassi Holali Degue, Jerome Le Ny
Summary: This note discusses the Kalman filtering problem under privacy constraints. A two-stage architecture is proposed to enforce differential privacy and handle sensitive data collected from multiple agents. The optimal aggregation stage is computed by solving a semidefinite program, and significant performance improvement is demonstrated compared to input perturbation schemes.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Mathematics, Applied
Takashi Furuya, Roland Potthast
Summary: This paper studies the inverse medium scattering problem and proposes two reconstruction algorithms based on the Kalman filter. These algorithms can avoid constructing large system equations and retain the information of past updates.
Article
Engineering, Electrical & Electronic
Tomer H. Hamam, Justin Romberg
Summary: This paper studies streaming optimization problems and proposes conditions for the convergence of solutions at a linear rate. It also presents a new efficient algorithm and provides example applications to support the theoretical results.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
J. Ghibaudo, M. Aucejo, O. De Smet
Summary: This paper introduces a novel Bayesian filter for estimating mechanical excitation sources from vibration measurements. The proposed filter unifies most of the state-of-the-art recursive filters developed in the last decade for solving input-state estimation problems. By assuming that the predicted input vector follows a generalized Gaussian distribution, the proposed filter promotes the spatial sparsity of the estimated input vector. Numerical and experimental evaluations show that the proposed filter, called Sparse adaptive Bayesian Filter, outperforms existing filters in terms of input estimation accuracy and avoidance of the drift effect.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Environmental Sciences
Bertrand Saulquin
Summary: BRDF estimation is essential for characterizing the anisotropic behavior of observed surfaces and normalizing satellite-derived observations. Implementing robust methods to handle noise in the reflectance data caused by atmospheric correction is crucial for accurate normalization. The Kalman filtering approach shows promise for achieving more suitable results compared to classical methods.
Article
Environmental Sciences
Shenghui Yang, Shiqiang Li, Xiaoxue Jia, Yonghua Cai, Yifei Liu
Summary: This paper proposes a translational motion compensation method for rapidly spinning targets by analyzing the correlation between echoes, achieving high-precision envelope alignment.
Article
Chemistry, Analytical
Chenyu Liu, Anlin Wang, Xiaotian Li, Xiaoxiang Li
Summary: This paper proposes a thermal load prediction model based on the Kalman filter algorithm, which can accurately predict the thermal load of a proportional solenoid valve under random load conditions. Historical samples of the solenoid valve's power and thermal load are obtained through experiments, and the k-means clustering algorithm is used to process the samples and obtain the prediction model. Experimental results show that the thermal load model based on the Kalman filter has higher prediction accuracy and adaptability, with a maximum prediction deviation of less than 5%.
Article
Materials Science, Multidisciplinary
William Fauriat
INTERNATIONAL JOURNAL OF FRACTURE
(2011)
Article
Engineering, Industrial
W. Fauriat, N. Gayton
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2014)
Proceedings Paper
Engineering, Mechanical
W. Fauriat, C. Mattrand, N. Gayton, A. Beakou
FATIGUE DESIGN 2015, INTERNATIONAL CONFERENCE PROCEEDINGS, 6TH EDITION
(2015)
