Home  About us  Contact
 

Estimation Error Covariance (estimation + error_covariance)

Distribution by Scientific Domains
Engineering75%

Selected Abstracts

Kalman filtering over wireless fading channels,How to handle packet drop,

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 18 2009
Yasamin Mostofi
Abstract In this paper we consider estimation of dynamical systems over wireless fading communication channels using a Kalman filter. We show the impact of the stochastic communication noise on the estimation process. We furthermore show how noisy packets should be handled in the receiver. More specifically, we illustrate the impact of the availability of a cross-layer information path on the optimum receiver design. In the absence of a cross-layer information path, it was shown that packet drop should be designed to balance information loss and communication noise in order to optimize the performance. In the presence of a cross-layer path, we show that keeping all the packets will minimize the average estimation error covariance. We also derive the stability condition in the presence of noisy packets and show that it is independent of the shape of the communication noise variance or availability of a cross-layer information path. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Model error and sequential data assimilation: A deterministic formulation

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 634 2008
A. Carrassi
Abstract Data assimilation schemes are confronted with the presence of model errors arising from the imperfect description of atmospheric dynamics. These errors are usually modelled on the basis of simple assumptions such as bias, white noise, and first-order Markov process. In the present work, a formulation of the sequential extended Kalman filter is proposed, based on recent findings on the universal deterministic behaviour of model errors in marked contrast with previous approaches. This new scheme is applied in the context of a spatially distributed system proposed by Lorenz. First, it is found that, for short times, the estimation error is accurately approximated by an evolution law in which the variance of the model error (assumed to be a deterministic process) evolves according to a quadratic law, in agreement with the theory. Moreover, the correlation with the initial condition error appears to play a secondary role in the short-time dynamics of the estimation error covariance. Second, the deterministic description of the model error evolution, incorporated into the classical extended Kalman filter equations, reveals that substantial improvements of the filter accuracy can be gained compared with the classical white-noise assumption. The universal short-time quadratic law for the evolution of the model error covariance matrix seems very promising for modelling estimation error dynamics in sequential data assimilation. Copyright © 2008 Royal Meteorological Society [source]

Robust unscented Kalman filtering for nonlinear uncertain systems

ASIAN JOURNAL OF CONTROL, Issue 3 2010
K. Xiong
Abstract A derivative-free robust Kalman filter algorithm is proposed for nonlinear uncertain systems. The unscented transform (UT) is adopted instead of the linearization technique to obtain the solution of the H, filter Riccati equation. A robust unscented Kalman filter (RUKF) is derived to guarantee an optimized upper bound on the estimation error covariance despite the model uncertainties and the approximation error of the UT. The proposed algorithm is applied to a satellite attitude determination system. Simulation results show that the RUKF is more effective than the unscented Kalman filter (UKF) in cases where alignment errors are present. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

A reduced-order stochastic observer approach to optimal state estimation with noise-free measurements

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 3 2001
Edwin Engin Yaz
Abstract In a continuous-time Kalman filter, it is required that the measurement noise covariance be non-singular. If the measurements are noise-free, then this condition does not hold and, in practice, the measurement data are differentiated to define a derived measurement function to build what is known as Deyst filter. It is proposed here that a reduced-order observer be used in deriving the linear minimum-variance filter to construct state estimates based on the original measurement data with no need for differentiation. This filter is of dimension (n,p) where n and p are the state and measurement vector dimensions, respectively. In this work, we consider both the finite-time and infinite-time results. The set of all assignable estimation error covariances are characterized and the set of all estimator gains are parametrized in addition to the linear minimum variance optimal results. The conditions for the existence of the optimal steady-state filter are obtained in terms of the system theoretic properties of the original signal model. A simple example is included to illustrate the effectiveness of the proposed technique. Copyright © 2001 John Wiley & Sons, Ltd. [source]