Deconvolution filtering where the system and noise dynamics are obtained by parametric system identification is considered. Consistent with standard identification methods, ellipsoidal uncertainty in the estimated parameters is considered. Three problems are considered: 1) Computation of the worst case H2 performance of a given deconvolution filter in this uncertainty set. 2) Design of a filter...

RFID-based location awareness is becoming the most important issue in many fields in recent years, such as ubiquitous computing, mobile computing. In indoor localization systems RSSI-based methods are usually used in office buildings. However, RSSI is susceptible to external influences, and performances unstably due to the environmental factors affecting signal propagation. In this paper, we pr...

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to dis...

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The use of orthonormal functions and consistency relationships to identify models of multivariable processes and their uncertainty descriptions, is discussed in this paper. The norm-bounded uncertainty can be obtained from the covariance matrix of the parameters. Using a-priori consistency relationships, unstructured uncertainty may be converted to a structured one. The μ analysis shows that th...

The problem of obtaining the underlying linear dynamic compliance matrix in the presence of nonlinearities in a general Multi-Degree-of-Freedom (MDOF) system can be solved using the Conditioned Reverse Path (CRP) method introduced by Richards and Singh (1998 Journal of Sound and Vibration, 213(4): p. 673-708). The CRP method also provides a means of identifying the coefficients of any nonlinear...

This paper addresses robust deconvolution filtering when the system and noise dynamics are obtained by parametric system identification. Consistent with standard identificationmethods, the uncertainty in the estimated parameters is represented by an ellipsoidal uncertainty region. Three problems are considered: (1) computation of the worst case H2 performance of a given deconvolution filter in ...

One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification methods. In this contribution we present a sufficient condition for the existence of a H∞ state feedback controller for the multi-input/...

Statistical matching (or data fusion) has long been used to merge separate data files in order to generate a joint fusion data set. Since the target joint data are not observable, it is recognized that, in addition to sampling variations that exist in the separate data files, there is an identification uncertainty associated with the assumptions that underpin the fusion procedure. In this paper...