A Tutorial on Bernoulli Filters: Theory, Implementation and Applications

By B. Ristic, B. Vo, Ba-Ngu Vo, Alfonso Farina. IEEE Transactions on Signal Processing, 2013

296 citations recorded by the source corpus.

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 or Gaussian 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, bearings-only tracking, passive radar/sonar surveillance, visual tracking, monitoring/prediction of an epidemic and tracking using natural language statements. More advanced topics of smoothing, multi-target detection/tracking, parameter estimation and sensor control are briefly reviewed with pointers for further reading.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/ad27eea002c27365aa9cd30d331243aaf0d2660e

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In Artificial Intelligence

1. Hao Zhu
2. Henry Leung
3. Ka-Veng Yuen
4. Lyudmila S. Mihaylova

In Civil and Structural Engineering

1. Ka-Veng Yuen
2. James L. Beck
3. Hao Sun
4. Oral Büyüköztürk
5. Z. Cao
6. Dianqing Li
7. Kok-Kwang Phoon

Citations in the corpus

• Extended Object Tracking: Introduction, Overview and Applications (2016, unknown): “The price of a more accurate model is an increased 5 Video with tracking results: https://youtu.be/sGTGNkrprts”
• Overview of Environment Perception for Intelligent Vehicles (2017, background): “In contrast to explicit data association, implicit data association tracking approaches output a set of object hypotheses in an implicit way, such as particle filter approaches [132], probability hypothesis density (PHD) filtering [133], multi-target multiBernoulli (MeMBer) filtering [134], [135], a”