Relevant bibliographies by topics / Unscented Kalman filter grand gain

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Academic literature on the topic 'Unscented Kalman filter grand gain'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Unscented Kalman filter grand gain"

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Matsuura, Tsubasa, Masahiro Matsushita, Gan Chen, and 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.

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He, Xiaoyou, Yu Su, and Yuhe Qiu. "An Improved Unscented Kalman Filter for Maneuvering Target Tracking*." Journal of Physics: Conference Series 2216, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2216/1/012010.

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Abstract The photoelectric pod provides angular information and distance information for the UAV (Unmanned Aerial Vehicle), and the UAV uses it to estimate the status information of the moving target. Since the measurement information of the photoelectric pod is the angle of sight and relative distance, the measurement equation contains some nonlinear functions in the Cartesian coordinate system, and the output frequency of the photoelectric pod is low. The improved unscented Kalman filter combines the function of prediction and correction, introduces the prior information of the target accele
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Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, and 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 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.

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Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extende
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Liang, Yunpei, Jiahui Dai, Kequan Wang, Xiaobo Li, and Pengcheng Xu. "A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor." Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9684382.

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This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore,
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Legowo, Ari, Zahratu H. Mohamad, and Hoon Cheol Park. "Mixed Unscented Kalman Filter and Differential Evolution for Parameter Identification." Applied Mechanics and Materials 256-259 (December 2012): 2347–53. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2347.

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This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industr
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Tehrani, Mohammad, Nader Nariman-zadeh, and Mojtaba Masoumnezhad. "Adaptive fuzzy hybrid unscented/H-infinity filter for state estimation of nonlinear dynamics problems." Transactions of the Institute of Measurement and Control 41, no. 6 (2018): 1676–85. http://dx.doi.org/10.1177/0142331218787607.

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In this paper, a new hybrid unscented Kalman (UKF) and unscented [Formula: see text](U[Formula: see text]F) filter is presented that can adaptively adjust its performance better than that of either UKF and/or U[Formula: see text], accordingly. In this way, two Takagi-Sugeno-Kang (TSK) fuzzy logic systems are presented to adjust automatically some weights that combine those UK and U[Formula: see text] filters, independent of the dynamics of the problem. Such adaptive fuzzy hybrid unscented Kalman/[Formula: see text] filter (AFUK[Formula: see text]) is based on the combination of gain, a priori
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Kim, DongBeom, Daekyo Jeong, Jaehyuk Lim, Sawon Min, and Jun Moon. "Application of Recurrent Neural-Network based Kalman Filter for Uncertain Target Models." Journal of the Korea Institute of Military Science and Technology 26, no. 1 (2023): 10–21. http://dx.doi.org/10.9766/kimst.2023.26.1.010.

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For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of
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Fan, Yongcun, Haotian Shi, Shunli Wang, Carlos Fernandez, Wen Cao, and Junhan Huang. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation." Energies 14, no. 8 (2021): 2268. http://dx.doi.org/10.3390/en14082268.

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This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algo
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Cao, Lu, Weiwei Yang, Hengnian Li, Zhidong Zhang, and Jianjun Shi. "Robust double gain unscented Kalman filter for small satellite attitude estimation." Advances in Space Research 60, no. 3 (2017): 499–512. http://dx.doi.org/10.1016/j.asr.2017.03.014.

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Wang, Junting, Tianhe Xu, and Zhenjie Wang. "Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation." Sensors 20, no. 1 (2019): 60. http://dx.doi.org/10.3390/s20010060.

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Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition t
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Dissertations / Theses on the topic "Unscented Kalman filter grand gain"

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Daid, Assia. "Sur la convergence d’unscented Kalman filter." Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0013.

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Notre travail consiste à étudier les propriétés du filtre unscented Kalman filter "UKF" et son adaptation en tant qu'observateur non linéaire. Il a été développé mais, bien que donnant en pratique de meilleurs résultats, aucune preuve de convergence n’existe qui garantit son utilisation.Un résultat négatif a été obtenu (sa non-convergence). Ce qui nous a conduit à proposer une nouvelle version : "unscented Kalman observer" pour les systèmes à temps continu dans un cadre déterministe. Nous avons montré sa convergence, lorsque les erreurs d'estimations initiales sont suffisamment petites. Cette
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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.

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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special repres
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Conference papers on the topic "Unscented Kalman filter grand gain"

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Bucci, Alessandro, Alessandro Ridolfi, Matteo Franchi, and 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.

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Zhang, Limin, Zengqiang Chen, and 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.

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Iglesias Jr, Cristovão F., Luis Pessoa, Claudio Miceli, and Miodrag Bolic. "Limitations of Joint and Dual Nonlinear Kalman Estimators in Low-Cost Bioprocess Monitoring." In LatinX in AI at International Conference on Machine Learning 2024. Journal of LatinX in AI Research, 2024. http://dx.doi.org/10.52591/lxai202407273.

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The biopharmaceutical industry constantly presses for fast and low-cost bioprocess monitoring strategies. However, a recent study has shown that the Joint Extended Kalman Filter (JEKF) is inefficient in this monitoring type under biomanufacturing conditions. This work investigates the Dual Extended Kalman Filter (DEKF), Joint Unscented Kalman Filter (JUKF), and Joint Cubature Kalman Filter (JCKF) under these challenging conditions. Our theoretical analysis also reveals inefficiencies in DEKF, while our empirical tests using a synthetic dataset indicate that JUKF and JCKF only perform well with
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Ceresoli, Michele, Giovanni Zanotti, and 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.

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In recent years, the Moon has been identified as a key testing ground to develop and enhance technologies for future deep-space missions, resulting in an ever-growing number of planned Moon-targeted launches from both space agencies and commercial actors. Despite having different objectives, all these users share the need to maintain accurate and reliable state estimates. In this regard, The European Space Agency (ESA) has launched the Moonlight initiative to foster the development of a dedicated Lunar Communication and Navigation System (LCNS) exploiting a small satellite constellation in lun
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