Multiple-model multiple hypothesis probability hypothesis density filter with blind zone

Tianli Ma, Xinmin Wang and Ting Li

International Journal of Industrial and Systems Engineering, 2017, vol. 27, issue 2, 180-195

Abstract: According to the problem of continuous tracking of multiple manoeuvring targets under the blind zone, a multiple-model probability hypothesis density (MM-PHD) filter based on the multiple hypothesis method is proposed. Under the blind zone, target state is estimated using the multiple model method. The hypothesis state is as the update state in the update step. Once the measurement is received, it will be updated using the MM filter. An improved pruning and merging algorithm is proposed to solve the problem of the number of hypothesis Gaussian components increasing continuously. Simulation results show that the novel algorithm is more effective in tracking multiple manoeuvring targets and improves performance of continuous target tracking.

Keywords: blind zone; multiple manoeuvring targets; Gaussian mixture; finite set statistics; intensity function; PHD; multiple model method; multiple hypothesis method; Bayesian theory. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=86271 (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:ids:ijisen:v:27:y:2017:i:2:p:180-195

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().