Add to ORKGIn this paper we present a class of bounded-uncertainty estimators as the solution of a classic estimation problem involving unknown statistics. The estimators are derived under the non-typical assumption of correlated signal and noise. The boundeduncertainty framework gives an additional degree of freedom for estimator design that can benefit its performance. It also provides an indirect way of verifying hypotheses regarding unknown variable statistics in a particular application domain by examining the behavior of the estimators as a function of the bound(s). If the unknown statistics are within a lower bound than the worst-case limit assumed by a classic minimax estimator, the quality of the estimation is increased by this new approach. ...
In this paper, we address the problem of designing matched filters which are robust against uncertai...
International audienceThis correspondence deals with an extension of minimum variance estimation whe...
In this paper we consider the problem of modelling observed data using a class of multivariate model...
Mathematical modelling under uncertainty together with the field of applied statistics represent too...
We formulate and solve a new parameter estimation problem in the presence of bounded model uncertain...
In this paper optimal algorithms for robust estimation and filtering are constructed. No statistical...
Estimating a function from noisy measurements is a crucial problem in statistics and engineering, wi...
A problem which often arises in statistical signal processing is the detection of a parameterized si...
International audienceEnergy detection is the simplest and non-coherent used approach, for spectrum ...
International audienceIn many applications, observations result from the random presence or absence ...
International audienceThe Compressed Sensing (CS) framework outperforms the sampling rate limits giv...
In the general signal+noise (allowing non-normal, non-independent observations) model, we construct ...
Nahi [2] considered the optimal linear estimation with uncertain observations. In his model, the bin...
International audienceCompressed sensing (CS) enables measurement reconstruction by using sampling r...
The problem of minimax estimation in the linear regression model is considered under the assumption ...
In this paper, we address the problem of designing matched filters which are robust against uncertai...
International audienceThis correspondence deals with an extension of minimum variance estimation whe...
In this paper we consider the problem of modelling observed data using a class of multivariate model...
Mathematical modelling under uncertainty together with the field of applied statistics represent too...
We formulate and solve a new parameter estimation problem in the presence of bounded model uncertain...
In this paper optimal algorithms for robust estimation and filtering are constructed. No statistical...
Estimating a function from noisy measurements is a crucial problem in statistics and engineering, wi...
A problem which often arises in statistical signal processing is the detection of a parameterized si...
International audienceEnergy detection is the simplest and non-coherent used approach, for spectrum ...
International audienceIn many applications, observations result from the random presence or absence ...
International audienceThe Compressed Sensing (CS) framework outperforms the sampling rate limits giv...
In the general signal+noise (allowing non-normal, non-independent observations) model, we construct ...
Nahi [2] considered the optimal linear estimation with uncertain observations. In his model, the bin...
International audienceCompressed sensing (CS) enables measurement reconstruction by using sampling r...
The problem of minimax estimation in the linear regression model is considered under the assumption ...
In this paper, we address the problem of designing matched filters which are robust against uncertai...
International audienceThis correspondence deals with an extension of minimum variance estimation whe...
In this paper we consider the problem of modelling observed data using a class of multivariate model...