@ARTICLE{Kolansky_Jeremy_Real-time_2013,
 author={Kolansky, Jeremy and Sandu, Corina},
 volume={vol. 60},
 number={No 1},
 journal={Archive of Mechanical Engineering},
 pages={7-21},
 howpublished={online},
 year={2013},
 publisher={Polish Academy of Sciences, Committee on Machine Building},
 abstract={Vehicle parameters have a significant impact on handling, stability, and rollover propensity. This study demonstrates two methods that estimate the inertia values of a ground vehicle in real-time. Through the use of the Generalized Polynomial Chaos (gPC) technique for propagating the uncertainties, the uncertain vehicle model outputs a probability density function for each of the variables. These probability density functions (PDFs) can be used to estimate the values of the parameters through several statistical methods. The method used here is the Maximum A-Posteriori (MAP) estimate. The MAP estimate maximizes the distribution of P(β|z) where β is the vector of the PDFs of the parameters and z is the measurable sensor comparison. An alternative method is the application of an adaptive filtering method. The Kalman Filter is an example of an adaptive filter. This method, when blended with the gPC theory is capable at each time step of updating the PDFs of the parameter distributions. These PDF’s have their median values shifted by the filter to approximate the actual values.},
 type={Artykuły / Articles},
 title={Real-time parameter estimation study for inertia properties of ground vehicles},
 URL={http://ochroma.man.poznan.pl/Content/84669/PDF/01_paper.pdf},
 doi={10.2478/meceng-2013-0001},
 keywords={parameter estimation, EKF, polynomial chaos, Bayesian statistics},
}