Probability hypothesis density filtering with sensor networks and irregular measurement sequences
Date
2010
Authors
Bishop, Adrian
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
The problem of multi-object tracking with sensor networks is studied using the probability hypothesis density filter. The sensors are assumed to generate signals which are sent to an estimator via parallel channels which incur independent delays. These signals may arrive out-of-order (out-of-sequence), be corrupted or even lost due to, e.g., noise in the communication medium and protocol malfunctions. In addition, there may be periods when the estimator receives no information. A closed-form, recursive solution to the considered problem is detailed that generalizes the Gaussian-mixture probability hypothesis density (GM-PHD) filter previously detailed in the literature.
Description
Keywords
Keywords: Delay tolerant; Irregular measurement sequences; Out of sequence measurements; PHD filtering; Random-set-based estimation; Estimation; Information fusion; Probability; Sensor networks Delay-tolerant PHD filtering; Irregular measurement sequences; Out-of-sequence measurements; PHD filtering; Random-set-based estimation; Sensor networks
Citation
Collections
Source
Proceedings of Fusion 2010
Type
Conference paper
Book Title
Entity type
Access Statement
License Rights
DOI
Restricted until
2037-12-31