Graduation date: 1999In this thesis, three model-based methods are presented for finding the location of a\ud point source with possibly time-varying strength for a class of distributed parameter systems.\ud The first method involves off-line numerical computation of the time-response data\ud at the sensor(s) from all possible source locations and functions of source strength, and\ud comparison of these data with actual measurements. The second method involves approximation\ud of the infinite-dimensional distributed parameter system by a finite-dimensional\ud lumped parameter system: the partial differential and/or integral equations describing\ud the distributed parameter system are replaced by a set of ordinary differential equations,\ud ...
We show that the sensor self-localization problem can be cast as a static parameter estimation probl...
We propose a distributed algorithm for sensor network localization, which is based upon a decomposit...
International audienceThis paper proposes a guaranteed robust bounded-error distributed estimation a...
Localizing sources of physical quantities is often only possible in an indirect manner by observing ...
In estimating parameters, a small sample with high information content is preferable to a large samp...
This paper addresses a systematic method for the reconstruction and the prediction of a distributed ...
This paper addresses the model-based localization of sensor networks based on local observations of ...
The aim of this paper is to consider the optimal, from the viewpoint of a state estimation accuracy,...
The performance of monitoring and control techniques for distributed-sensor systems is affected by t...
This thesis surveys methods for determining sensor locations which maximize the achievable accuracy ...
A primary challenge for the reconstruction of continuous-time, continuous-amplitude distributed para...
Identifying the parameters with the largest influence on the predicted outputs of a model revealswhi...
Abstract—We show that the sensor self-localization problem can be cast as a static parameter estimat...
Space-time continuous phenomena such as pollution loads or temperature distributions often originate...
In this paper, we have considered distributed bounded-error state estimation applied to the problem ...
We show that the sensor self-localization problem can be cast as a static parameter estimation probl...
We propose a distributed algorithm for sensor network localization, which is based upon a decomposit...
International audienceThis paper proposes a guaranteed robust bounded-error distributed estimation a...
Localizing sources of physical quantities is often only possible in an indirect manner by observing ...
In estimating parameters, a small sample with high information content is preferable to a large samp...
This paper addresses a systematic method for the reconstruction and the prediction of a distributed ...
This paper addresses the model-based localization of sensor networks based on local observations of ...
The aim of this paper is to consider the optimal, from the viewpoint of a state estimation accuracy,...
The performance of monitoring and control techniques for distributed-sensor systems is affected by t...
This thesis surveys methods for determining sensor locations which maximize the achievable accuracy ...
A primary challenge for the reconstruction of continuous-time, continuous-amplitude distributed para...
Identifying the parameters with the largest influence on the predicted outputs of a model revealswhi...
Abstract—We show that the sensor self-localization problem can be cast as a static parameter estimat...
Space-time continuous phenomena such as pollution loads or temperature distributions often originate...
In this paper, we have considered distributed bounded-error state estimation applied to the problem ...
We show that the sensor self-localization problem can be cast as a static parameter estimation probl...
We propose a distributed algorithm for sensor network localization, which is based upon a decomposit...
International audienceThis paper proposes a guaranteed robust bounded-error distributed estimation a...