@inbook{6740b7afdff04eb09d04620a147117c3,
title = "Particle Filtering Combined with Interval Methods for Tracking Applications",
abstract = "This chapter introduces a new approach for sequential nonlinear estimation based on a combination of particle filtering and interval analysis for tracking applications. The method is first presented within the interval framework, by introducing a new concept of box particles for the purposes of drastically reducing the number of particles. In this chapter, the box particle filter (Box-PF) is presented in its original ad hoc formulation. The chapter provides an overview of the Bayesian inference methodology. The chapter gives a theoretical derivation of the Box-PF as a sum of uniform probability density functions (pdfs). It demonstrates the advantages of the Box-PF over a dynamic localization example. The contributions and open issues for future works are summarized in the chapter.",
keywords = "Bayesian filtering, Box particle filter (Box-PF), Interval methods, Tracking applications, Uniform probability density functions (pdfs)",
author = "Amadou Gning and Lyudmila Mihaylova and Fahed Abdallah and Branko Ristic",
year = "2016",
month = may,
day = "31",
doi = "10.1002/9781118450550.ch02",
language = "English",
isbn = "9781118450550",
series = "Integrated Tracking, Classification, and Sensor Management: Theory and Applications",
publisher = "Wiley-IEEE Press",
pages = "43--74",
booktitle = "Integrated Tracking, Classification, and Sensor Management: Theory and Applications",
}