@inproceedings{b0d395f2ca014dd1af23ae08b95cf8d8,
title = "A sequential Monte Carlo approximation of the HISP filter",
abstract = "A formulation of the hypothesised filter for independent stochastic populations (hisp) is proposed, based on the concept of association measure, which is a measure on the set of observation histories. Using this formulation, a particle approximation is introduced at the level of the association measure for handling the exponential growth in the number of underlying hypotheses. This approximation is combined with a sequential Monte Carlo implementation for the underlying single-object distributions to form a mixed particle association model. Finally, the performance of this approach is compared against a Kalman filter implementation on simulated data based on a finite-resolution sensor.",
keywords = "finite-resolution sensor, Multi-object filtering",
author = "Jeremie Houssineau and Clark, {Daniel E} and {Del Moral}, Pierre",
year = "2015",
doi = "10.1109/EUSIPCO.2015.7362584",
language = "English",
isbn = "9780992862633",
series = "Proceedings of the European Signal Processing Conference (EUSIPCO)",
publisher = "IEEE",
pages = "1251--1255",
booktitle = "2015 23rd European Signal Processing Conference (EUSIPCO)",
address = "United States",
note = "23rd European Signal Processing Conference 2015, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
}