2,344 research outputs found
Robust Hypothesis Testing with a Relative Entropy Tolerance
This paper considers the design of a minimax test for two hypotheses where
the actual probability densities of the observations are located in
neighborhoods obtained by placing a bound on the relative entropy between
actual and nominal densities. The minimax problem admits a saddle point which
is characterized. The robust test applies a nonlinear transformation which
flattens the nominal likelihood ratio in the vicinity of one. Results are
illustrated by considering the transmission of binary data in the presence of
additive noise.Comment: 14 pages, 5 figures, submitted to the IEEE Transactions on
Information Theory, July 2007, revised April 200
A Contraction Analysis of the Convergence of Risk-Sensitive Filters
A contraction analysis of risk-sensitive Riccati equations is proposed. When
the state-space model is reachable and observable, a block-update
implementation of the risk-sensitive filter is used to show that the N-fold
composition of the Riccati map is strictly contractive with respect to the
Riemannian metric of positive definite matrices, when N is larger than the
number of states. The range of values of the risk-sensitivity parameter for
which the map remains contractive can be estimated a priori. It is also found
that a second condition must be imposed on the risk-sensitivity parameter and
on the initial error variance to ensure that the solution of the risk-sensitive
Riccati equation remains positive definite at all times. The two conditions
obtained can be viewed as extending to the multivariable case an earlier
analysis of Whittle for the scalar case.Comment: 22 pages, 6 figure
Robust State Space Filtering under Incremental Model Perturbations Subject to a Relative Entropy Tolerance
This paper considers robust filtering for a nominal Gaussian state-space
model, when a relative entropy tolerance is applied to each time increment of a
dynamical model. The problem is formulated as a dynamic minimax game where the
maximizer adopts a myopic strategy. This game is shown to admit a saddle point
whose structure is characterized by applying and extending results presented
earlier in [1] for static least-squares estimation. The resulting minimax
filter takes the form of a risk-sensitive filter with a time varying risk
sensitivity parameter, which depends on the tolerance bound applied to the
model dynamics and observations at the corresponding time index. The
least-favorable model is constructed and used to evaluate the performance of
alternative filters. Simulations comparing the proposed risk-sensitive filter
to a standard Kalman filter show a significant performance advantage when
applied to the least-favorable model, and only a small performance loss for the
nominal model
Robust Kalman Filtering: Asymptotic Analysis of the Least Favorable Model
We consider a robust filtering problem where the robust filter is designed
according to the least favorable model belonging to a ball about the nominal
model. In this approach, the ball radius specifies the modeling error tolerance
and the least favorable model is computed by performing a Riccati-like backward
recursion. We show that this recursion converges provided that the tolerance is
sufficiently small
Prediction of the Spectrum of a Digital Delta–Sigma Modulator Followed by a Polynomial Nonlinearity
This paper presents a mathematical analysis of the power spectral density of the output of a nonlinear block driven by a digital delta-sigma modulator. The nonlinearity is a memoryless third-order polynomial with real coefficients. The analysis yields expressions that predict the noise floor caused by the nonlinearity when the input is constant
Quantum Stochastic Processes: A Case Study
We present a detailed study of a simple quantum stochastic process, the
quantum phase space Brownian motion, which we obtain as the Markovian limit of
a simple model of open quantum system. We show that this physical description
of the process allows us to specify and to construct the dilation of the
quantum dynamical maps, including conditional quantum expectations. The quantum
phase space Brownian motion possesses many properties similar to that of the
classical Brownian motion, notably its increments are independent and
identically distributed. Possible applications to dissipative phenomena in the
quantum Hall effect are suggested.Comment: 35 pages, 1 figure
Kalman filtering and Riccati equations for descriptor systems
Projet META2The theory of Kalman filtering is extended to the case of systems with descriptor dynamics. Explicit expressions are obtained for this descriptor Kalman filter allowing for the possible singularity of the observation noise covariance. Asymptotic behavior of the filter in the time-invariant case is studied ; in particular, a method for constructing the solution of the algebraic descriptor Riccati equation is presented
Graph structure and recursive estimation of noisy linear relations
Projet META2This paper examines estimation problems specified by noisy linear relations describing either dynamical models or measurements. Each such problem has a graph structure, which can be exploited to derive recursive estimation algorithms only when the graph is acyclic, i.e. when it is obtained by combining disjoint trees. Aggregation techniques appropriate for reducing an arbitrary graph to an acyclic one are presented. The recursive maximum likelihood estimation procedures that we present are based on two elementary operations, called reduction and extraction, which are used to compress successive observations and discard unneeded variables. These elementary operations are used to derive filtering and smoothing formulas applicable to both linear and arbitrary trees, which are in turn applicable to estimation problems in settings ranging from 1-D descriptor systems to 2-D difference equations to multiscal models of random fields. These algorithms can beviewed as direct generalizations to a far richer setting of Kalman filtering and both two-filter and Rauch-Tung-Striebel smoothing for standard causal state space models
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