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The problem of estimating the state of discrete-time linear systems when uncertainties affect the system matrices is addressed. A quadratic cost function is considered, involving a finite number of recent measurements and a prediction vector. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar...
The problem of model-based fault detection in the presence of both parametric uncertainty and noise is addressed in this paper. Intervals are used to represent the uncertainty in the system parameters and interval extensions of parity equations are used as adaptive threshold selectors. A proper combination in time of different (interval) parity equations, together with a robust indicator, is used...
This paper deals with the general problem of the estimation of physical parameters in the presence of nuisance parameters for which an explicit characterisation of the uncertainty is available. Within a Bayesian context, the maximum a posteriori estimation is used. The asymptotic confidence ellipsoid is then determined. The projection of the latter onto the parameter axes is used to obtain the confidence...
This paper presents a methodology for enhancing the robustness of a GPC controlled system by convex optimisation of the Youla parameter. This methodology requires, as a first step, the design of an initial GPC controller; this controller is then robustified considering frequency and temporal constraints. By means of the Youla parametrisation, frequency and temporal constraints are formulated within...
We describe a method for textural feature extraction of MRI volumes of the brain and, based upon those features, a method for classification and assessment of the anatomical malformations of the brain, due to Alzheimer's Disease (AD). In our research, we make the hypothesis that there is enough detectable textural evidence from a 3D analysis of MR images of the brain to detect and identify the earliest...
Diffusion MRI (dMRI) provides a noninvasive tool for investigating white matter tracts. Probabilistic fiber tracking has been proposed to represent the fiber structures as 3D streamlines while taking the uncertainty introduced by noise into account. In this paper, we propose a probabilistic fiber tracking method based on bootstrapping a multi-tensor model with a fixed tensor basis. The fiber orientation...
We report about a GPS receiver relative calibration campaign, which took place between five European National Metrology Institutes or Designated Institutes: LNE-SYRTE in Observatoire de Paris (Paris, France), where the reference receiver of the campaign was located, ROA (San Fernando, Spain), SP (Borås, Sweden), PTB (Braunschweig, Germany) and INRIM (Torino, Italy). We used as traveling equipment...
In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax...
In this paper we investigate optimal sensor placement for time-difference-of-arrival localization. The minimization of estimation uncertainty is used as the optimization criterion, which is equivalent to maximization of the determinant of the Fisher information matrix. It is shown that for equal sensor noise variances equiangular sensor separation is an optimal localization geometry irrespective of...
High time resolution of ultra wideband (UWB) signals facilitates very precise positioning capabilities based on time-of-arrival (TOA) measurements. Although the theoretical lower bound for TOA estimation can be achieved by the maximum likelihood principle, it is impractical due to the need for extremely high-rate sampling and the presence of large number of multipath components. On the other hand,...
We treat the problem of designing a robust estimation filter to recover a stationary random signal x[n] convolved with a linear time-invariant (LTI) filter h[n] and corrupted by additive stationary noise, in the presence of spectra uncertainties. Our approach is based on minimizing the worst-case difference between the MSE in the presence of uncertainties, and the MSE of the Wiener filter that knows...
Spectrum sensing is one of the most crucial tasks a cognitive radio has to carry out in order to decide whether a licensed spectrum can be used without causing interference to the licensed or the Primary Users (PU). Many techniques exist in the literature for satisfactory detection performance. However, in many cases, an additive white Gaussian noise is assumed, which is an oversimplified assumption...
New filters are derived for estimating the n-dimensional state of a linear dynamic system based on uncertain m-dimensional observations, which suffer from two types of uncertainties simultaneously. The first uncertainty is a stochastic process with given distribution. The second uncertainty is only known to be bounded, the exact underlying distribution is unknown. The new estimators combine set theoretic...
This paper introduces a mechanism for testing multivariable models on which model-based controllers are designed. Although external excitation is not necessary, the data collection includes a stage where the controller is switched to open-loop operation (manual mode). The main idea is to measure a certain "distance" between closed-loop and open-loop signals, and then trigger a flag if this...
H∞ identification of model sets for linear, time-invariant, discrete-time, BIBO stable SISO systems is here considered, assuming a known bound on the transfer-function derivative magnitude as prior information on the system to be identified. Experimental information consists of a finite number of measurements in the frequency domain, corrupted by a pointwise bounded additive noise. The aim is to deliver...
Algorithms for Robust Fault Detection and Identification (RFDI) in dynamic systems strive to be sensitive to faults in plant components, while being insensitive to plant and noise model uncertainties. In the field of signal detection with applications such as radar and medical images, sensitivity to signals of interest is also an objective, while robustness is concerned with undesirable interferences...
This paper1 presents a method to estimate a Decision Feedback Equalizer (DFE) directly from training data, which is robust w.r.t. time-variations in the communication channel. It is based on the indirect method proposed in [15], where the time variations in the channel are modeled as a probabilistic uncertainty. The robust DFE optimizes the performance by minimizing the mean squared error averaged...
Ranging signals in the urban environment suffer from significant attenuation and additive errors caused by reflections. Special design considerations for ranging-based positioning systems that operate in the Urban Canyon are presented. The tradeoff between acquisition sensitivity and acquisition time is presented. The integrity of the measurements is improved by verifying that the signals exhibit...
The paper deals with the problem of robust fault diagnosis of industrial systems. The main objective was to develop fault diagnosis scheme based on the parameters identification via Bounded-Error Approach. The effectiveness of the proposed approach was shown on the model of brushless DC motor.
A minimax LQG control approach is used to synthesize a controller which minimizes noise level at a specified location in an acoustic duct. This controller is robust against uncertainties introduced by neglecting the higher order modes of the duct.
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