In this paper different filtering techniques for nonlinear state estimation are explored and compared. We distinguish between approaches that approximate the nonlinear function (extended Kalman filter) and other approaches approximating the distribution of measurements and state (unscented Kalman filter and sequential Monte Carlo filter). The paper is showing both, the algorithms and simulated ...

We consider a class of nonlinear estimation problems possessing certain algebraic properties, and we exploit these properties in order to study the computational complexity of nonlinear estimation algorithms. Specifically, we define a class of finite-state Markov processes evolving on finite groups and consider noisy observations of these processes. By introducing some concepts from the theory ...

In this paper two nonlinear modelling approaches are employed to derive single nonlinear models for a Rolls Royce aircraft gas turbine. The first approach is based on the estimation of a NARMAX model using conventional structure selection and parameter estimation techniques, and the second approach is based on the use of feedforward Multilayer-Perceptron (MLP) neural networks. The performances ...

We study efficient estimation for models with nonlinear heteroscedasticity. In two-step quantile regression heteroscedastic models, motivated by several undesirable issues caused the preliminary estimator, we propose an estimator constrainedly weighting information across quantiles. When weights are optimally chosen under certain constraints, new can simultaneously eliminate effect of as well a...

OF THE DISSERTATION Performance Analysis and Enhancement of OFDM-based WLAN Systems in the Presence of Nonlinear HPAs and Narrowband Interference for Single and Multiple Transmit Antennas by David Wei-Ting Chi Doctor of Philosophy in Electrical Engineering (Communication Theory and Systems) University of California San Diego, 2008 Professor Pankaj Das, Chair Professor Rene L. Cruz, Co-Chair Thi...

Recent work has examined the estimation of models of stimulus-driven neural activity in which a linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a noisy, leaky, integrate-and-fire mechanism with a spike-dependent after-current. We have formulated this problem in terms of ...

considering the nonlinear hyperelastic or viscoelastic nature of soft tissues has an important effect on modeling results. in medicalapplications, accounting nonlinearity begets an ill posed problem, due to absence of external force. myocardium can be consideredas a hyperelastic material, and variational approaches are proposed to estimate stiffness matrix, which take into account the linearand...