Relevant bibliographies by topics / Parameter identification and estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Parameter identification and estimation'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Parameter identification and estimation"

1

Kulczycki, Piotr. "An Algorithm for Bayes Parameter Identification." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (1999): 611–14. http://dx.doi.org/10.1115/1.1409552.

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This paper deals with the task of parameter identification using the Bayes estimation method, which makes it possible to take into account the differing consequences of positive and negative estimation errors. The calculation procedures are based on the kernel estimators technique. The final result constitutes a complete algorithm usable for obtaining the value of the Bayes estimator on the basis of an experimentally obtained random sample. An elaborated method is provided for numerical computations.
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Kaji, Tetsuya. "Theory of Weak Identification in Semiparametric Models." Econometrica 89, no. 2 (2021): 733–63. http://dx.doi.org/10.3982/ecta16413.

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We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, that is, when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is ineffic
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Yanuar, Ferra, Putri Trisna Sari, and Yudiantri Asdi. "IDENTIFICATION OF RAINFALL DISTRIBUTION IN WEST SUMATERA AND ASSESSMENT OF ITS PARAMETERS USING BAYES METHOD." MEDIA STATISTIKA 13, no. 2 (2020): 161–69. http://dx.doi.org/10.14710/medstat.13.2.161-169.

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One distribution of rainfall data is a lognormal distribution with location parameters and scale parameters . This study aims to estimate the mean and variance of rainfall data in several selected cities and regencies in West Sumatra. Parameter estimation is estimated by using maximum likelihood estimation (direct method) and Bayes method. This study resulted that the Bayes method produces a better predictive value with a smaller variance value than with direct estimation. It was concluded that the estimation by the Bayes method was a better estimator method than the direct estimation.
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VOSS, HENNING U., JENS TIMMER, and JÜRGEN KURTHS. "NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS." International Journal of Bifurcation and Chaos 14, no. 06 (2004): 1905–33. http://dx.doi.org/10.1142/s0218127404010345.

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We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regression approaches, noisy measurements can produce inaccurate parameter estimates. Another problem is that for chaotic systems the cost functions that have to be minimized to estimate states and parameters are so complex that common optimization routines may fail. We show that the inclusion of information
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Feng, Juqiang, Long Wu, Kaifeng Huang, Xing Zhang, and Jun Lu. "State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification." E3S Web of Conferences 194 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202019402023.

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Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy
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Li, Da, Lu Liu, Chuanxu Yue, Xiaojin Gao, and Yunhai Zhu. "Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range." Energies 18, no. 7 (2025): 1866. https://doi.org/10.3390/en18071866.

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The state of charge (SOC) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of SOC estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and SOC assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter fil
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Yu, Yih-Choung, J. R. Boston, Marwan Simaan, and James F. Antaki. "Identification Scheme for Cardiovascular Parameter Estimation." IFAC Proceedings Volumes 29, no. 1 (1996): 920–25. http://dx.doi.org/10.1016/s1474-6670(17)57781-2.

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Skibinski, G. L., and W. A. Sethares. "Thermal parameter estimation using recursive identification." IEEE Transactions on Power Electronics 6, no. 2 (1991): 228–39. http://dx.doi.org/10.1109/63.76809.

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Mageed Hag Elamin, Khalid Abd El. "Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise." Current Research in Statistics & Mathematics 3, no. 3 (2024): 01–20. http://dx.doi.org/10.33140/crsm.03.03.01.

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This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the
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Dorraki, M., M. S. Islam, A. Allison, and D. Abbott. "Parameter identification using moment of velocity." Royal Society Open Science 6, no. 11 (2019): 190671. http://dx.doi.org/10.1098/rsos.190671.

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Many physical systems can be adequately modelled using a second-order approximation. Thus, the problem of system identification often reduces to the problem of estimating the position of a single pair of complex–conjugate poles. This paper presents a convenient but approximate technique for the estimation of the position of a single pair of complex–conjugate poles, using the moment of velocity (MoV). The MoV is a Hilbert transform based signal processing tool that addresses the shortcomings of instantaneous frequency. We demonstrate that the MoV can be employed for parameter identification of
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Dissertations / Theses on the topic "Parameter identification and estimation"

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Martinsson, Jesper. "Ultrasonic measurement principles : modeling, identification, and parameter estimation /." Luleå : Luleå University of Technology, 2008. http://epubl.luth.se/1402-1544/2008/37.

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Zhou, Wenliang. "Multivariate analysis in vibration modal parameter identification /." View online ; access limited to URI, 2006. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3248248.

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Hemakumara, Madu Prasad. "UAV Parameter Estimation with Gaussian Process Approximations." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9414.

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Unmanned Aerial Vehicles (UAVs) provide an alternative to manned aircraft for risk associated missions and applications where sizing constraints require miniaturized flying platforms. UAVs are currently utilised in an array of applications ranging from civilian research to military battlegrounds. A part of the development process for UAVs includes constructing a flight model. This model can be used for modern flight controller design and to develop high fidelity flight simulators. Furthermore, it also has a role in analysing stability, control and handling qualities of the platform. Developing
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Vazirinejad, Shamsedin. "Model identification and parameter estimation of stochastic linear models." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185037.

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It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errors-in-variables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errors-in-variables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however,
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Chou, I.-Chun. "Parameter estimation and network identification in metabolic pathway systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26513.

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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Voit, Eberhard O.; Committee Member: Borodovsky, Mark; Committee Member: Butera, Robert; Committee Member: Kemp, Melissa; Committee Member: Park, Haesun. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Bennia, Abdelhak. "Mimo systems parameters identification." Thesis, Virginia Tech, 1986. http://hdl.handle.net/10919/41579.

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<p>In this thesis, a presentation of a new canonical representation of multi-input multioutput systems is given. The new characterization covers the full range of practical situations in linear systems according to the structural properties and model of the perturbations which are known. Its direct link to ARMA processes as well as to classical state space representation ls also given.</p> <p> The importance of the new representation lies in the fact that all unknown parameters and state variables appear linearly multlplied by either external variables (inputs and outputs) that appear in the
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Cooper, Matthew. "Bayesian system identification for nonlinear dynamical vehicle models." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213212/1/Matthew_Cooper_Thesis.pdf.

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This thesis investigates the use of novel Bayesian system identification techniques to estimate unknown parameters in nonlinear vehicle dynamics. In the first part of this thesis, a dual merging particle filter is proposed that accurately estimates non-Gaussian posterior parameter distributions for different vehicle models. In the second part of this thesis, a novel myopic sequential technique is proposed to design informative experiments for estimating the unknown parameters of a real-world robotic vehicle. This myopic technique is extended in the last part of the thesis to incorporate a roll
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Chase, Adam Thomas. "Flight Testing Small UAVs for Aerodynamic Parameter Estimation." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1191.

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A flight data acquisition system was developed to aid unmanned vehicle designers in verifying the vehicle's design performance. The system is reconfigurable and allows the designer to choose the correct combination of complexity, risk, and cost for a given flight test. The designer can also reconfigure the system to meet packaging and integration requirements. System functionality, repeatbility, and accuracy was validated by collecting data during multiple flights of a radio-controlled aircraft. Future work includes sensor fusion, thrust prediction methods, stability and control derivative est
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Lau, Wing-yi. "New recursive parameter estimation algorithms in impulsive noise environment with application to frequency estimation and system identification." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37595866.

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Pollard, Stephen J. "Recursive parameter identification for estimating and displaying maneuvering vessel path." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FPollard.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2003.<br>Thesis advisor(s): Roberto Cristi, Fotis A. Papoulias. Includes bibliographical references (p. 155). Also available online.
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Books on the topic "Parameter identification and estimation"

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Pieter, Eykhoff, Parks P. C, International Federation of Automatic Control., International Federation of Operational Research Societies., and IFAC/IFORS Symposium on Identifaction and System Parameter Estimation, (8th : 1988 : Beijing), eds. Identification and system parameter estimation. Pergamon, 1990.

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Maine, Richard E. Identification of dynamic systems: Theory and formulation. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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Walter, E. Identification of parametric models from experimental data. Springer, 1997.

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Hucklebridge, Arthur A. Identification of structural interface characteristics using component mode synthesis. National Aeronautics and Space Administration, 1987.

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C, Murphy Patrick. A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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Iliff, Kenneth W. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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Facility, Dryden Flight Research, ed. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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Iliff, Kenneth W. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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1950-, Chase Rory L., ed. Automatic identification: An IFS executive briefing. IFS Publications, 1988.

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Walter, E. Identifiability of parametric models. Pergamon Press, 1987.

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Book chapters on the topic "Parameter identification and estimation"

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Boyadjiev, Christo. "Parameter Identification (Estimation)." In Theoretical Chemical Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10778-8_8.

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Subrahmanyam, Allamaraju, and Ganti Prasada Rao. "Robust Parameter Estimation." In Identification of Continuous-Time Systems. CRC Press, 2019. http://dx.doi.org/10.1201/9780429352850-4.

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Sun, Ne-Zheng, and Alexander Sun. "Model Structure Identification." In Model Calibration and Parameter Estimation. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_7.

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Simoen, E., and G. Lombaert. "Bayesian Parameter Estimation." In Identification Methods for Structural Health Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32077-9_4.

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Haber, Robert, and László Keviczky. "Parameter Estimation Methods." In Nonlinear System Identification — Input-Output Modeling Approach. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4481-0_3.

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Isermann, Rolf, and Marco Münchhof. "Parameter Estimation in Closed-Loop." In Identification of Dynamic Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_13.

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Isermann, Rolf, and Marco Münchhof. "Parameter Estimation for Frequency Responses." In Identification of Dynamic Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_14.

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Isermann, Rolf, and Marco Münchhof. "Parameter Estimation for MIMO Systems." In Identification of Dynamic Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_17.

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Isermann, Rolf, and Marco Münchhof. "Practical Aspects of Parameter Estimation." In Identification of Dynamic Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_23.

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Kozin, F. "Structural Parameter Identification Techniques." In Analysis and Estimation of Stochastic Mechanical Systems. Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-2820-6_4.

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Conference papers on the topic "Parameter identification and estimation"

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Oyanagi, Masahiro, Shigeo Morimoto, Yukinori Inoue, and Masayuki Sanada. "Position Estimation and Parameter Identification of SynRM at Standstill." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10920987.

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Li, Jingxuan, Zhe Lin, Jingyu Qian, Jie Wu, Yan Wei, and Fengzhong Qu. "Synchronization Signal Identification and Parameter Estimation Based on Delay Autocorrelation Method." In OCEANS 2024 - SINGAPORE. IEEE, 2024. http://dx.doi.org/10.1109/oceans51537.2024.10682353.

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Zhang, Xiaoyong, Wenyun Li, Siqi Ruan, et al. "State of Charge Estimation Based on Physical Information Neural Network Parameter Identification." In 2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific). IEEE, 2024. http://dx.doi.org/10.1109/itecasia-pacific63159.2024.10738611.

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Hebert, Anthony, Sergio Cafarelli, Paul Mackin, and Jonathan Shaver. "Parameter Identification through Adaptive Optimal Estimation." In AIAA Modeling and Simulation Technologies Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1548.

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Jachan, M., F. Hlawatsch, and G. Matz. "Linear methods for TFARNA parameter estimation and system approximation." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628723.

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Enescu, M., and V. Koivunen. "Parameter estimation in a class of nonlinear state - space models." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628594.

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Renaux, A., P. Forster, and P. Larzabal. "A new derivation of the bayesian bounds for parameter estimation." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628659.

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Dub, M. "Parameter Identification - The random initial estimation issue." In 2011 14th International Conference on Mechatronics. IEEE, 2011. http://dx.doi.org/10.1109/mechatron.2011.5961086.

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Kolansky, Jeremy, Corina Sandu, and Schalk Els. "Tire-Ground Normal Force Estimation From Vehicle System Identification and Parameter Estimation." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34812.

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Ground vehicle stability controllers can be significantly improved through knowledge of the vehicle’s tire-ground normal force. This work demonstrates a proof-of-concept study of such an estimator. The method involves work from two previous methods that perform real-time estimation of the vehicle’s mass and horizontal CG position. Inclusion of the previous work provides the foundation for a comprehensive method that estimates the tire-ground normal load of a ground vehicle that is invariant with respect to the vehicle parameters.
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De Angelis, V., L. Izzo, A. Napolitano, and M. Tanda. "Cyclostationarity-based parameter estimation of wide-band signals in mobile communications." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628748.

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Reports on the topic "Parameter identification and estimation"

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Gardner, William A. Exploitation of Cyclostationarity for Signal-Parameter Estimation and System Identification. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada267137.

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Cooper, Russell. Estimation and Identification of Structural Parameters in the Presence of Multiple Equilibria. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/w8941.

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Maier, Robert S. Simulation and Parameter Estimation. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada299449.

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Goncalves, Paulo, and Rudolf Riedi. Diverging Moments and Parameter Estimation. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada486761.

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Vojnovic, Milan, and Seyoung Yun. Parameter Estimation for Thurstone Choice Models. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1353026.

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Casey, Stephen D. Number Theoretic Methods in Parameter Estimation. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada346613.

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Culioli, J., and V. Protopopescu. Parameter identification for generalized Lanchester's equations. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7196009.

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Banks, H. T., and Kathleen L. Bihari. Modeling and Estimating Uncertainty in Parameter Estimation. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada447550.

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Antonova, P. V., R. M. Gizdatullin, and M. M. Kazakov. Ensemble Meta-Model for SMO Parameter Estimation. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2025. https://doi.org/10.12731/ofernio.2025.25456.

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Reed, Brendan, Cassandra Armstrong, Rahul Somasundaram, et al. Direct Nuclear Parameter Estimation from Gravitational Waves. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2570007.

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