Box particle filtering for nonlinear state estimation using interval analysis

Abdallah, Fahed; Gning, Amadou; Bonnifait, Philippe

doi:10.1016/j.automatica.2007.07.024

Cited by 127 publications

(109 citation statements)

References 10 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…Several contractors exist, each works in a different manner and is efficient only for specific CSPs and for certain cases [13,14,15]. "Intervalization with Gauss elimination" [4] is an important class of CSPs for which intervalization of finite subsolvers can be employed only if the system is formed of linear interval equations and if all elements on the diagonal of the matrix are different than zero.…”

Section: Types Of Contractorsmentioning

confidence: 99%

A Contribution to the Evaluation of Imprecise Availability of Complex Systems Using Markov Models

Akrouche¹,

Sallak²,

Châtelet³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Types Of Contractorsmentioning

confidence: 99%

A Contribution to the Evaluation of Imprecise Availability of Complex Systems Using Markov Models

Akrouche¹,

Sallak²,

Châtelet³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various methods have been put forward to robustify the particle filtering algorithm for systems with unknown statistics. The box particle filtering, which combines sequential Monte Carlo method with interval analysis, has been introduced in [41][42][43]. Unlike the standard particle filtering method where particles are points in the state space and likelihood functions are defined by a statistical model, the box particle filter uses multidimensional intervals in the state space as particles and a bounded error model to evaluate the likelihood functions.…”

Section: Robust Particle Filtermentioning

confidence: 99%

Particle filtering with applications in networked systems: a survey

Wang

Yuan

2016

Complex Intell. Syst.

View full text Add to dashboard Cite

The particle filtering algorithm was introduced in the 1990s as a numerical solution to the Bayesian estimation problem for nonlinear and non-Gaussian systems and has been successfully applied in various fields including physics, economics, engineering, etc. As is widely recognized, the particle filter has broad application prospects in networked systems, but network-induced phenomena and limited computing resources have led to new challenges to the design and implementation of particle filtering algorithms. In this survey paper, we aim to review the particle filtering method and its applications in networked systems. We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress inThe work of W. Li, Y. Yuan and L.

show abstract

“…The entire area is been divided into many row and columns. This defines a particular box for a respective row and column [15]. So this two dimensional box is used as a field area in which the estimation of the current position of the Target is been confined to particular boxes.…”

Section: Fig 1 Sensing Range Of Sensormentioning

confidence: 99%

Ant-Colony-based Algorithm for Multi-Target Tracking in Mobile Sensor Networks

BarathKumar¹,

Myilsamy²

2013

IJCA

View full text Add to dashboard Cite

Target tracking is one of the applications of Mobile Sensor Networks. Mobility management is the important parameter that affects the performance and lifetime of the Mobile sensor networks. So we need to manage the mobility in a controlled manner. Existing methods attempt to achieve these requirements for controlled mobility single target tracking only. In this paper, we propose a Multi-Target Tracking method using Ant Colony Optimization to satisfy these requirements. In this proposed method, targets current position values are estimated at every time step. Then, predicting the next position value of each target by using the previous time-step estimated values. Interval Analysis is used for estimation and prediction of position values. Then the proposed method consists of moving the mobile node in an optimal way to cover Multi-Target. The optimal path is been chosen by Ant Colony Optimization technique. Simulations results shows the advantages of the proposed method compared to single target tracking methods.

show abstract

Box particle filtering for nonlinear state estimation using interval analysis

Cited by 127 publications

References 10 publications

A Contribution to the Evaluation of Imprecise Availability of Complex Systems Using Markov Models

A Contribution to the Evaluation of Imprecise Availability of Complex Systems Using Markov Models

Particle filtering with applications in networked systems: a survey

Ant-Colony-based Algorithm for Multi-Target Tracking in Mobile Sensor Networks

Contact Info

Product

Resources

About