@inproceedings{5252a598022149798d338689db8ad05f,
title = "Probability hypothesis density filtering with sensor networks and irregular measurement sequences",
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.",
keywords = "Delay-tolerant PHD filtering, Irregular measurement sequences, Out-of-sequence measurements, PHD filtering, Random-set-based estimation, Sensor networks",
author = "Bishop, {Adrian N.}",
year = "2010",
doi = "10.1109/icif.2010.5711952",
language = "English",
isbn = "9780982443811",
series = "13th Conference on Information Fusion, Fusion 2010",
publisher = "IEEE Computer Society",
booktitle = "13th Conference on Information Fusion, Fusion 2010",
address = "United States",
}