1 IRIT Université Paul Sabatier – 31062 Toulouse Cedex 4 – France. Email: {benferhat, dubois, prade}@irit.fr Abstract: This paper proposes a qualitative counterpart of Kalman filtering in the possibilistic logic setting. It corresponds to a type of updating involving a prediction step followed by a revision step. This is compared with updating operations based on imaging in the sense of Lewis. ...

Particle filtering is a sequential Monte Carlo technique that recursively computes the posterior probability density function using the concept of “Importance Sampling”. This paper considers the application of particle filtering technique to a target tracking application, in which a radar sends a signal towards a target and estimates the state (position and velocity) of the target using the obs...

A 4-dimensional ensemble Kalman filter method (4DEnKF), which adapts ensemble Kalman filtering to the assimilation of observations that are asynchronous with the analysis cycle, is discussed. In the ideal case of linear dynamics between consecutive analyses, the algorithm is equivalent to Kalman filtering assimilation at each observation time. Tests of 4DEnKF on the Lorenz 40 variable model are...

The Kalman filter is used in this paper as a framework for space time data analysis. Using Kalman filtering it is possible to include physically based simulation models into the data analysis procedure. Attention is concentrated on the development of fast filter algorithms to make Kalman filtering feasible for high dimensional space time models. The ensemble Kalman filter and the reduced rank s...

in this paper, we present an application of the stochastic calculusto the problem of modeling electrical networks. the filtering problem have animportant role in the theory of stochastic differential equations(sdes). in thisarticle, we present an application of the continuous kalman-bucy filter for a rlcircuit. the deterministic model of the circuit is replaced by a stochastic model byadding a ...

This paper establishes a connection between the unknown input observer problem (also known as the exact input-decoupling filtering problem) and the Kalman filtering problem. Such a connection leads to a better understanding of filtering. As a consequence of this, tools for the analysis as well as synthesis of filters can be developed. Moreover, such tools can be utilized to establish performanc...

Many state estimation and control algorithms require knowledge of how probability distributions propagate through dynamical systems. However, despite hybrid systems becoming increasingly important in many fields, there has been little work on utilizing the map transitions. Here, we make use a propagation law that employs saltation matrix (a first-order update to sensitivity equation) create Sal...