Nonlinear Estimation With State-Dependent Gaussian Observation Noise
نویسندگان
چکیده
منابع مشابه
Real-time Recursive State Estimation for Nonlinear Discrete Dynamic Systems with Gaussian or non-Gaussian Noise
Many systems in the real world are more accurately described by nonlinear models. Since the original work of Kalman (Kalman, 1960; Kalman & Busy, 1961), which introduces the Kalman filter for linear models, extensive research has been going on state estimation of nonlinear models; but there do not yet exist any optimum estimation approaches for all nonlinear models, except for certain classes o...
متن کاملMinimum Entropy Parameter Estimation of Bounded Nonlinear Dynamic Systems with Non-Gaussian State and Measurement Noise
Parameter estimation plays an important role in Systems Biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more complex as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. In this paper a novel stochastic parameter estimation method has been developed where the entropy of the joint res...
متن کاملNonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise
Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonline...
متن کاملState Estimation in the Presence of non-Gaussian Noise
The problem of nonlinear filtering with a non-Gaussian model of measurement errors is considered in this paper: Based on Bayes classification of the observations an approximate solution is introduced. The Bayesian estimator can be applied to any discrete time, lineal; or nonlinear system which is observed in additive non-Gaussian measurement noise. The problem of narrowband inte$erence suppress...
متن کاملNonlinear and Non-gaussian State Estimation: a Quasi-optimal Estimator
The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hürzeler and Künsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on nonlinearity and non...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2010
ISSN: 0018-9286,1558-2523
DOI: 10.1109/tac.2010.2042006