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Rao-Blackwellized Particle Filters with Out-of-Sequence Measurement Processing

Berntorp, Karl LU ; Robertsson, Anders LU and Årzén, Karl-Erik LU (2014) In IEEE Transactions on Signal Processing 62(24). p.6454-6467
Abstract
This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of... (More)
This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of computation time and tracking performance. Both algorithms yield estimation improvements when compared with recent particle filter algorithms for OOSM processing; in the considered examples they achieve up to 10% enhancements in estimation accuracy. In some cases the proposed algorithms even deliver accuracy that is similar to the lower performance bounds. Because the considered setup is common in various estimation scenarios, the developed algorithms enable improvements in different types of filtering applications. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Tracking, particle filtering, out-of-sequence measurement (OOSM), Rao-Blackwellization
in
IEEE Transactions on Signal Processing
volume
62
issue
24
pages
6454 - 6467
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000345516000009
  • scopus:84913583600
ISSN
1053-587X
DOI
10.1109/TSP.2014.2365763
language
English
LU publication?
no
id
0c1958ee-21d9-4964-af39-31fdab2eeafb (old id 4697847)
date added to LUP
2014-10-15 09:58:39
date last changed
2017-11-05 03:09:46
@article{0c1958ee-21d9-4964-af39-31fdab2eeafb,
  abstract     = {This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of computation time and tracking performance. Both algorithms yield estimation improvements when compared with recent particle filter algorithms for OOSM processing; in the considered examples they achieve up to 10% enhancements in estimation accuracy. In some cases the proposed algorithms even deliver accuracy that is similar to the lower performance bounds. Because the considered setup is common in various estimation scenarios, the developed algorithms enable improvements in different types of filtering applications.},
  author       = {Berntorp, Karl and Robertsson, Anders and Årzén, Karl-Erik},
  issn         = {1053-587X},
  keyword      = {Tracking,particle filtering,out-of-sequence measurement (OOSM),Rao-Blackwellization},
  language     = {eng},
  number       = {24},
  pages        = {6454--6467},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Signal Processing},
  title        = {Rao-Blackwellized Particle Filters with Out-of-Sequence Measurement Processing},
  url          = {http://dx.doi.org/10.1109/TSP.2014.2365763},
  volume       = {62},
  year         = {2014},
}