8,690 research outputs found
A new family of high-resolution multivariate spectral estimators
In this paper, we extend the Beta divergence family to multivariate power
spectral densities. Similarly to the scalar case, we show that it smoothly
connects the multivariate Kullback-Leibler divergence with the multivariate
Itakura-Saito distance. We successively study a spectrum approximation problem,
based on the Beta divergence family, which is related to a multivariate
extension of the THREE spectral estimation technique. It is then possible to
characterize a family of solutions to the problem. An upper bound on the
complexity of these solutions will also be provided. Simulations suggest that
the most suitable solution of this family depends on the specific features
required from the estimation problem
Convergence analysis of a family of robust Kalman filters based on the contraction principle
In this paper we analyze the convergence of a family of robust Kalman
filters. For each filter of this family the model uncertainty is tuned
according to the so called tolerance parameter. Assuming that the corresponding
state-space model is reachable and observable, we show that the corresponding
Riccati-like mapping is strictly contractive provided that the tolerance is
sufficiently small, accordingly the filter converges
Rational approximations of spectral densities based on the Alpha divergence
We approximate a given rational spectral density by one that is consistent
with prescribed second-order statistics. Such an approximation is obtained by
minimizing a suitable distance from the given spectrum and under the
constraints corresponding to imposing the given second-order statistics. Here,
we consider the Alpha divergence family as a distance measure. We show that the
corresponding approximation problem leads to a family of rational solutions.
Secondly, such a family contains the solution which generalizes the
Kullback-Leibler solution proposed by Georgiou and Lindquist in 2003. Finally,
numerical simulations suggest that this family contains solutions close to the
non-rational solution given by the principle of minimum discrimination
information.Comment: to appear in the Mathematics of Control, Signals, and System
Robust Kalman Filtering under Model Perturbations
We consider a family of divergence-based minimax approaches to perform robust
filtering. The mismodeling budget, or tolerance, is specified at each time
increment of the model. More precisely, all possible model increments belong to
a ball which is formed by placing a bound on the Tau-divergence family between
the actual and the nominal model increment. Then, the robust filter is obtained
by minimizing the mean square error according to the least favorable model in
that ball. It turns out that the solution is a family of Kalman like filters.
Their gain matrix is updated according to a risk sensitive like iteration where
the risk sensitivity parameter is now time varying. As a consequence, we also
extend the risk sensitive filter to a family of risk sensitive like filters
according to the Tau-divergence family
Multivariate Spectral Estimation based on the concept of Optimal Prediction
In this technical note, we deal with a spectrum approximation problem arising
in THREE-like multivariate spectral estimation approaches. The solution to the
problem minimizes a suitable divergence index with respect to an a priori
spectral density. We derive a new divergence family between multivariate
spectral densities which takes root in the prediction theory. Under mild
assumptions on the a priori spectral density, the approximation problem, based
on this new divergence family, admits a family of solutions. Moreover, an upper
bound on the complexity degree of these solutions is provided
An Interpretation of the Dual Problem of the THREE-like Approaches
Spectral estimation can be preformed using the so called THREE-like approach.
Such method leads to a convex optimization problem whose solution is
characterized through its dual problem. In this paper, we show that the dual
problem can be seen as a new parametric spectral estimation problem. This
interpretation implies that the THREE-like solution is optimal in terms of
closeness to the correlogram over a certain parametric class of spectral
densities, enriching in this way its meaningfulness
On the Robustness of the Bayes and Wiener Estimators under Model Uncertainty
This paper deals with the robust estimation problem of a signal given noisy
observations. We assume that the actual statistics of the signal and
observations belong to a ball about the nominal statistics. This ball is formed
by placing a bound on the Tau-divergence family between the actual and the
nominal statistics. Then, the robust estimator is obtained by minimizing the
mean square error according to the least favorable statistics in that ball.
Therefore, we obtain a divergence family-based minimax approach to robust
estimation. We show in the case that the signal and observations have no
dynamics, the Bayes estimator is the optimal solution. Moreover, in the dynamic
case, the optimal offline estimator is the noncausal Wiener filter
On the Effects of Battery Imperfections in an Energy Harvesting Device
Energy Harvesting allows the devices in a Wireless Sensor Network to recharge
their batteries through environmental energy sources. While in the literature
the main focus is on devices with ideal batteries, in reality several
inefficiencies have to be considered to correctly design the operating regimes
of an Energy Harvesting Device (EHD). In this work we describe how the
throughput optimization problem changes under \emph{real battery} constraints
in an EHD. In particular, we consider imperfect knowledge of the state of
charge of the battery and storage inefficiencies, \emph{i.e.}, part of the
harvested energy is wasted in the battery recharging process. We formulate the
problem as a Markov Decision Process, basing our model on some realistic
observations about transmission, consumption and harvesting power. We find the
performance upper bound with a real battery and numerically discuss the novelty
introduced by the real battery effects. We show that using the \emph{old}
policies obtained without considering the real battery effects is strongly
sub-optimal and may even result in zero throughput.Comment: In Proc. IEEE International Conference on Computing, Networking and
Communications, pp. 942-948, Feb. 201
Evaluation of shear mounted elastomeric damper
Viton-70 elastomeric shear mounted damper was built and tested on a T-55 power turbine spool in the rotor's high speed balancing rig. This application of a shear mounted elastomeric damper demonstrated for the first time, the feasibility of using elastomers as the primary rotor damping source in production turbine engine hardware. The shear damper design was selected because it was compatible with actual gas turbine engine radial space constraints, could accommodate both the radial and axial thrust loads present in gas turbine engines, and was capable of controlled axial preload. The shear damper was interchangeable with the production T-55 power turbine roller bearing support so that a direct comparison between the shear damper and the production support structure could be made. Test results show that the Viton-70 elastomer damper operated successfully and provided excellent control of both synchronous and nonsynchronous vibrations through all phases of testing up to the maximum rotor speed of 16,000 rpm. Excellent correlation between the predicted and experienced critical speeds, mode shapes and log decrements for the power turbine rotor and elastomer damper assembly was also achieved
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