Probability hypothesis density filtering with sensor networks and irregular measurement sequences
Description
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,...[Show more]
dc.contributor.author | Bishop, Adrian | |
---|---|---|
dc.coverage.spatial | Edinburgh Scotland | |
dc.date.accessioned | 2015-12-10T23:06:45Z | |
dc.date.created | July 26-29 2010 | |
dc.identifier.isbn | 9780982443811 | |
dc.identifier.uri | http://hdl.handle.net/1885/62790 | |
dc.description.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. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | International Conference on Information Fusion (FUSION 2010) | |
dc.source | Proceedings of Fusion 2010 | |
dc.subject | 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 | |
dc.title | Probability hypothesis density filtering with sensor networks and irregular measurement sequences | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2010 | |
local.identifier.absfor | 090609 - Signal Processing | |
local.identifier.ariespublication | u4334215xPUB737 | |
local.type.status | Published Version | |
local.contributor.affiliation | Bishop, Adrian, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 1 | |
local.bibliographicCitation.lastpage | 6 | |
local.identifier.absseo | 810104 - Emerging Defence Technologies | |
dc.date.updated | 2016-02-24T11:02:49Z | |
local.identifier.scopusID | 2-s2.0-79952379353 | |
Collections | ANU Research Publications |
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