Literatura científica selecionada sobre o tema "High gain unscented Kalman filter"
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Artigos de revistas sobre o assunto "High gain unscented Kalman filter"
Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey e Vinny Junior Foba Kakeu. "A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data". Applied Computational Intelligence and Soft Computing 2022 (10 de maio de 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.
Texto completo da fonteLv, Jiechao, Baochen Jiang, Xiaoli Wang, Yirong Liu e Yucheng Fu. "Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm". Electronics 9, n.º 9 (2 de setembro de 2020): 1425. http://dx.doi.org/10.3390/electronics9091425.
Texto completo da fonteRiva, Mauro H., Matthias Dagen e Tobias Ortmaier. "Adaptive High-Gain observer for joint state and parameter estimation: A comparison to Extended and Unscented Kalman filter". IFAC Proceedings Volumes 47, n.º 3 (2014): 8558–63. http://dx.doi.org/10.3182/20140824-6-za-1003.01609.
Texto completo da fonteBagheri, Ahmad, Shahram Azadi e Abbas Soltani. "A combined use of adaptive sliding mode control and unscented Kalman filter estimator to improve vehicle yaw stability". Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 231, n.º 2 (7 de outubro de 2016): 388–401. http://dx.doi.org/10.1177/1464419316673960.
Texto completo da fonteChen, Yudi, Xiangyu Liu, Changqing Li, Jiao Zhu, Min Wu e Xiang Su. "UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering". Electronics 13, n.º 6 (12 de março de 2024): 1054. http://dx.doi.org/10.3390/electronics13061054.
Texto completo da fonteMatsuura, Tsubasa, Masahiro Matsushita, Gan Chen e Isao Takami. "Gain-scheduled Control Using Unscented Kalman Filter". Proceedings of Conference of Tokai Branch 2019.68 (2019): 316. http://dx.doi.org/10.1299/jsmetokai.2019.68.316.
Texto completo da fonteGao, She Sheng, Wen Hui Wei e Li Xue. "Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm". Applied Mechanics and Materials 411-414 (setembro de 2013): 931–35. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.931.
Texto completo da fonteXu, Daxing, Bao Wang, Lu Zhang e Zhiqiang Chen. "A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise". Electronics 11, n.º 12 (13 de junho de 2022): 1863. http://dx.doi.org/10.3390/electronics11121863.
Texto completo da fonteXi, Yan Hui, e Hui Peng. "Training Multi-Layer Perceptrons with the Unscented Kalman Particle Filter". Advanced Materials Research 542-543 (junho de 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.745.
Texto completo da fonteLi, Chengyi, e Chenglin Wen. "A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model". Actuators 13, n.º 5 (1 de maio de 2024): 169. http://dx.doi.org/10.3390/act13050169.
Texto completo da fonteTeses / dissertações sobre o assunto "High gain unscented Kalman filter"
Daid, Assia. "Sur la convergence d’unscented Kalman filter". Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0013.
Texto completo da fonteThe present thesis is a study of the convergence of the unscented Kalman filter. A convergence analysis of the modified unscented Kalman filter ( used as an observer for a class of nonlinear deterministic continuous time systems, is presented. Under certain conditions, the extended Kalman filter ( is an exponential observer for non linear systems, i.e., the dynamics of the estimation error is exponentially stable. It is shown that unlike the EKF, the UKF is not an expo nentially converging observer. A modification of the UKF the unscented Kalman observer ( is proposed, which is a better candidate for an observer, we proved the exponentialconvergence of the UKO and also shown that the high gain UKF observer as a compromise between the high gain extended Kalman filter (HG EKF) and the high gain unscented Kalman filter (HG UKF). All these properties are illustrated on the example of the binary distillation column and on an example of geolocalization
Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications". Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.
Texto completo da fonteAkhtar, Jahanzeb. "Particle tracking using the unscented Kalman filter in high energy physics experiments". Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11482.
Texto completo da fontePaulsen, Trevor H. "Low cost/high precision flight dynamics estimation using the square-root unscented Kalman filter /". Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3181.pdf.
Texto completo da fontePaulsen, Trevor H. "Low Cost/ High Precision Flight Dynamics Estimation Using the Square-Root Unscented Kalman Filter". BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1922.
Texto completo da fonteFeddaoui-Papin, Aïda. "Observateurs non linéaires pour les systèmes à mesures asynchrones : application robotique mobile". Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL0008.
Texto completo da fonteThe aim of observability studies and observer design is to reconstruct the state of a dynamic system using the measurements available. In particular, the Kalman filter algorithm is considered. This widely-studied and used observer exists in several versions : for linear or nonlinear systems, for discrete, continuous or even continuous-discrete time, in the stochastic or deterministic framework. However, Most of the time, these observers are used with the assumption that the measurements provided by the sensors are synchronous. Most of the time, this assumption can be far from the physical reality, in particular when dealing with robotic systems. In this memoir, an observer tailored for nonlinear continuous-discrete asynchronous systems is presented. These systems are made of a continuous state equation and a multirate sampled output equation. Based on the existing high-gain Extended Kalman Filter for continuous nonlinear systems and continuous-discrete nonlinear systems with synchronous outputs, we develop an ad-hoc formalism and design an observer with a deterministic point of view. Its convergence is proven analytically and illustrated by an application on a mobile robotic system
Capítulos de livros sobre o assunto "High gain unscented Kalman filter"
Yerra, Yashwant, D. Ram Kumar Reddy e P. Sudheesh. "An Unscented Kalman Filter Approach for High-Precision Indoor Localization". In Intelligent Manufacturing and Energy Sustainability, 433–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4443-3_42.
Texto completo da fonteBianconi, Fortunato, Gabriele Lillacci e Paolo Valigi. "Dynamic Modeling and Parameter Identification for Biological Networks". In Handbook of Research on Computational and Systems Biology, 478–510. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-491-2.ch021.
Texto completo da fonteTrabalhos de conferências sobre o assunto "High gain unscented Kalman filter"
Ceresoli, Michele, Giovanni Zanotti e Michèle Lavagna. "Leveraging Sensors Fusion to Enhance One-way Lunar Navigation Signals". In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-201.
Texto completo da fonteBucci, Alessandro, Alessandro Ridolfi, Matteo Franchi e Benedetto Allotta. "Covariance and Gain-based Federated Unscented Kalman Filter for Acoustic-Visual-Inertial Underwater Navigation". In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705843.
Texto completo da fonteZhang, Limin, Zengqiang Chen e Xinghui Zhang. "A novel varible gain unscented kalman filter and its application in the integrated navigation system". In 2012 10th World Congress on Intelligent Control and Automation (WCICA 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcica.2012.6358056.
Texto completo da fonteZhang, Xinming, Bo Yang, Shan Li e Aidong Men. "An Unscented Kalman Filter for ICI Cancellation in High-Mobility OFDM System". In 2011 IEEE Vehicular Technology Conference (VTC 2011-Spring). IEEE, 2011. http://dx.doi.org/10.1109/vetecs.2011.5956315.
Texto completo da fonteYin, Haohao, Weiwei Xia, Yueyue Zhang e Lianfeng Shen. "UWB-based indoor high precision localization system with robust unscented Kalman filter". In 2016 IEEE International Conference on Communication Systems (ICCS). IEEE, 2016. http://dx.doi.org/10.1109/iccs.2016.7833646.
Texto completo da fonteShen, Kelei, Hongyu Ni, Qiang Lu, Jidong Cai e Wenxu Yan. "Power System State Estimation Based on Improved Strong Tracking Unscented Kalman Filter". In 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). IEEE, 2022. http://dx.doi.org/10.1109/ichve53725.2022.9961457.
Texto completo da fonteWang, Wen-jing, Xi Chen, Shuai Han, Wei-xiao Meng e Yi Zhang. "Unscented Kalman filter with open-loop compensation for high dynamic GNSS carrier tracking". In International Conference on Space Information Technology 2009, editado por Xingrui Ma, Baohua Yang e Ming Li. SPIE, 2009. http://dx.doi.org/10.1117/12.855176.
Texto completo da fonteXu, Longyuan, Peng Tong e Yinsheng Wei. "Sequential Multi-model Unscented Kalman Filter for Shipborne High Frequency Surface Wave Radar". In 2023 IEEE International Radar Conference (RADAR). IEEE, 2023. http://dx.doi.org/10.1109/radar54928.2023.10371192.
Texto completo da fonteBu, Xiangyuan, Weiping Zeng e Yanbo Wu. "High Dynamic Pseudo-Random Code Tracking Using Unscented Kalman Filter and Carrier-Aiding Technology". In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.478.
Texto completo da fonteLu, Peng, Timothy Sandy e Jonas Buchli. "Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control". In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8970476.
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