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Storage Efficient Particle Filters with Multiple Out-of-Sequence Measurements

Berntorp, Karl LU ; Årzén, Karl-Erik LU and Robertsson, Anders LU (2012) 15th International Conference on Information Fusion In 15th International Conference on Information Fusion (FUSION), 2012 p.471-478
Abstract
A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
15th International Conference on Information Fusion (FUSION), 2012
pages
471 - 478
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
15th International Conference on Information Fusion
external identifiers
  • Scopus:84867637663
ISBN
978-1-4673-0417-7
project
ENGROSS
language
English
LU publication?
yes
id
4527a1d5-cf21-43c4-a353-8a544541a02c (old id 2837293)
date added to LUP
2012-06-27 08:57:29
date last changed
2016-10-13 04:50:19
@misc{4527a1d5-cf21-43c4-a353-8a544541a02c,
  abstract     = {A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by exploiting the complete in-sequence information approach and extending it to nonlinear systems. Simulation experiments on a challenging nonlinear tracking scenario show that the new approach outperforms recent state-of-the-art particle filter algorithms in some respects, despite demanding less storage requirements.},
  author       = {Berntorp, Karl and Årzén, Karl-Erik and Robertsson, Anders},
  isbn         = {978-1-4673-0417-7},
  language     = {eng},
  pages        = {471--478},
  publisher    = {ARRAY(0x867d7e0)},
  series       = {15th International Conference on Information Fusion (FUSION), 2012},
  title        = {Storage Efficient Particle Filters with Multiple Out-of-Sequence Measurements},
  year         = {2012},
}