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Construction and comparison of approximations for switching linear gaussian state space models

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Bayesian Approach for Indoor Pedestrian Localisation

Michel Bierlaire, Bertrand Merminod

The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the us ...

2006

A Monte-Carlo method for initializing distributed tracking algorithms

Volkan Cevher

Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor networks. In this paper, we determine an initial probability distribution of multiple target states in a distributed manner t ...

2006

Stable Directed Belief Propagation in Gaussian DAGs using the auxiliary variable trick

We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of ...

IDIAP2005

Construction and comparison of approximations for switching linear gaussian state space models

Bertrand Mesot

We introduce a new method for approximate inference in Hybrid Dynamical Graphical models, in particular, for switching dynamical networks. For the important special case of switching linear Gaussian state space models (switching Kalman Filters), our method ...

IDIAP2005

Tracking People in Meetings with Particles

Daniel Gatica-Perez, Jean-Marc Odobez, Guillaume Lathoud, Silèye Oumar Ba, Kevin Smith

Automatic meeting analysis is an emerging research field. In this paper, we present stochastic algorithms for tracking people in multi-sensor meeting rooms, for a number of relevant tasks, including tracking multiple people, tracking head pose towards anal ...

2005

Model calibration: How to integrate statistical uncertainty and multi-objective equivalence?

André Musy, Bettina Schaefli

Hydrological model calibration should consider two different concepts of predictive uncertainty referring respectively to the statistical uncertainty and the so-called multi-objective equivalence. Currently available studies present different methods to qu ...

2005

General state-space models

Thomas Gsponer

In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...

EPFL2004

Hydrological behaviour through experimental and modelling approaches

Daniela Balin

The present work is related to the recent research topics in hydrology devoted to the integration of field knowledge into the hydrological modelling. The study catchment is the Haute-Mentue experimental basin (12.5 km2) located in western Switzerland, in t ...

EPFL2004

Tracking People in Meetings with Particles

Daniel Gatica-Perez, Jean-Marc Odobez, Guillaume Lathoud, Silèye Oumar Ba, Kevin Smith

IDIAP2004

The Scrambled Net Filter

Thomas Gsponer

The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...

2002