71,538 research outputs found
State estimation with partially observed inputs: a unified Kalman filtering approach
For linear stochastic time-varying state space models with Gaussian noises, this paper investigates state estimation for the
scenario where the input variables of the state equation are not fully observed but rather the input data is available only at an
aggregate level. Unlike the existing filters for unknown inputs that are based on the approach of minimum-variance unbiased
estimation, this paper does not impose the unbiasedness condition for state estimation; instead it incorporates a Bayesian
approach to derive a modified Kalman filter by pooling the prior knowledge about the state vector at the aggregate level
with the measurements on the output variables at the original level of interest. The estimated state vector is shown to be a
minimum-mean-square-error estimator. The developed filter provides a unified approach to state estimation: it includes the
existing filters obtained under two extreme scenarios as its special cases, i.e., the classical Kalman filter where all the inputs
are observed and the filter for unknown inputs
Simultaneous excitation and parameter identification for non-linear structural systems
In this paper, an algorithm is proposed for simultaneous excitation and parameter identification for non-linear system in state space. The algorithm is based on the sequential application of extended Kalman estimator for non-linear structural parameters and the weighted least squares estimation for unknown excitations. The state and parameter are reformed into the augmented state, and the state space equations are non-linear associated with the augmented state. With the first-order Taylor expansion for nonlinear system and approximately linear minimum-variance unbiased estimation, a recursive algorithm is derived where the identification of the augmented state and the excitation are interconnected. Two numerical examples which identify uncertain parameters of a 3-DOF Duffing-type system and a four-story hysteretic shear-beam building subject to unknown random excitation respectively, are conducted to demonstrate the effectiveness of the proposed approach
Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances
A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady-state Kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov equation method for robustness analysis is proposed. Their robust accuracy relations are proved. A simulation example verifies their robustness and accuracy relations
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Effect of embedded unbiasedness on discrete-time optimal FIR filtering estimates
Unbiased estimation is an efficient alternative to optimal estimation when the noise statistics are not fully known and/or the model undergoes temporary uncertainties. In this paper, we investigate the effect of embedded unbiasedness (EU) on optimal finite impulse response (OFIR) filtering estimates of linear discrete time-invariant state-space models. A new OFIR-EU filter is derived by minimizing the mean square error (MSE) subject to the unbiasedness constraint. We show that the OFIR-UE filter is equivalent to the minimum variance unbiased FIR (UFIR) filter. Unlike the OFIR filter, the OFIR-EU filter does not require the initial conditions. In terms of accuracy, the OFIR-EU filter occupies an intermediate place between the UFIR and OFIR filters. Contrary to the UFIR filter which MSE is minimized by the optimal horizon of N opt points, the MSEs in the OFIR-EU and OFIR filters diminish with N and these filters are thus full-horizon. Based upon several examples, we show that the OFIR-UE filter has higher immunity against errors in the noise statistics and better robustness against temporary model uncertainties than the OFIR and Kalman filters
Энергосберегающее устройство нагружения резервных электрогенераторов
The Kalman filter computes the minimum variance state estimate as a linear function of measurements in the case of a linear model with Gaussian noise processes. There are plenty of examples of non-linear estimators that outperform the Kalman filter when the noise processes deviate from Gaussianity, for instance in target tracking with occasionally maneuvering targets. Here we present, in a preliminary study, a detailed analysis of the well-known parameter estimation problem. This time with Gaussian mixture measurement noise. We compute the discrepancy of the best linear unbiased estimator BLUE and the Cramer-Rao lower bound, and based on this conclude when computationally intensive Kalman filter banks or particle filters may be used to improve performance
A Unified Filter for Simultaneous Input and State Estimation of Linear Discrete-time Stochastic Systems
In this paper, we present a unified optimal and exponentially stable filter
for linear discrete-time stochastic systems that simultaneously estimates the
states and unknown inputs in an unbiased minimum-variance sense, without making
any assumptions on the direct feedthrough matrix. We also derive input and
state observability/detectability conditions, and analyze their connection to
the convergence and stability of the estimator. We discuss two variations of
the filter and their optimality and stability properties, and show that filters
in the literature, including the Kalman filter, are special cases of the filter
derived in this paper. Finally, illustrative examples are given to demonstrate
the performance of the unified unbiased minimum-variance filter.Comment: Preprint for Automatic
Combining information in statistical modelling
How to combine information from different sources is becoming an important statistical area of research under the name of Meta Analysis. This paper shows that the estimation of a parameter or the forecast of a random variable can also be seen as a process of combining information. It is shown that this approach can provide sorne useful insights on the robustness properties of sorne statistical procedures, and it also allows the comparison of statistical models within a common framework. Sorne general combining rules are illustrated using examples from ANOVA analysis, diagnostics in regression, time series forecasting, missing value estimation and recursive estimation using the Kalman Filter
Framework for state and unknown input estimation of linear time-varying systems
The design of unknown-input decoupled observers and filters requires the
assumption of an existence condition in the literature. This paper addresses an
unknown input filtering problem where the existence condition is not satisfied.
Instead of designing a traditional unknown input decoupled filter, a
Double-Model Adaptive Estimation approach is extended to solve the unknown
input filtering problem. It is proved that the state and the unknown inputs can
be estimated and decoupled using the extended Double-Model Adaptive Estimation
approach without satisfying the existence condition. Numerical examples are
presented in which the performance of the proposed approach is compared to
methods from literature.Comment: This paper has been accepted by Automatica. It considers unknown
input estimation or fault and disturbances estimation. Existing approaches
considers the case where the effects of fault and disturbance can be
decoupled. In our paper, we consider the case where the effects of fault and
disturbance are coupled. This approach can be easily extended to nonlinear
system
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