Publications

Version: 1.1 Approved

LDO-ID 2014-02-25-03

Title A Tutorial on Bernoulli Filters: Theory, Implementation and Applications

Abstract

Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive estimation of dynamic systems, recently emerged from the random set theoretical framework. The common feature of Bernoulli filters is that they are designed for stochastic dynamic systems which randomly switch on and off. The applications are primarily in target tracking, where the switching process models target appearance or disappearance from the surveillance volume. The concept, however, is applicable to a range of dynamic phenomena, such as epidemics, pollution, social trends, etc. Bernoulli filters in general have no analytic solution and are implemented as particle filters orGaussian sum filters. This tutorial paper reviews the theory of Bernoulli filters as well as their implementation for different measurement models. The theory is backed up by applications in sensor networks, bearingsonly tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multitarget detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading.

Authors Ristic Branko, Vo Ba-Tuong, Vo Ba-Ngu, Farina Alfonso

Type Paper for Specialistic Magazine

Media IEEE Transactions on Signal Processing (Volume: 61, Issue: 13, July 2013)

Web site

Anno 2013