A particle filter approach to estimating target location using Brownian bridges

Jesse A. Nunez, Dashi I. Singham and Michael P. Atkinson

Journal of the Operational Research Society, 2020, vol. 71, issue 4, 589-605

Abstract: We study the problem of modelling the trajectory of a moving object of interest, or target, given limited locational and temporal information. Because of uncertainty in information, the location of the target can be represented using a spatial distribution, or heatmap. This paper proposes a comprehensive method for constructing and updating probability heatmaps for the location of a moving object based on uncertain information. This method uses Brownian bridges to model and construct temporal probability heatmaps of target movement, and employs a particle filter to update the heatmap as new intelligence arrives. This approach allows for more complexity than simple deterministic motion models, and is computationally easier to implement than detailed models for local target movement.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1570806 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:71:y:2020:i:4:p:589-605

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2019.1570806

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().