The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Particle filters for state estimation of jump Markov linear systems
Abstract
Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Density Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,525
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Self-citationSequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
Self-citationWiley Series in Probability and Statistics
(2008) · Wiley series in probability and statistics · doi:10.1002/series1345 · Cited by 4,217
Anderson, B. D. O., Moore, J. B., & Eslami, M. (1982) · IEEE Transactions on Systems Man and Cybernetics · Cited by 3,195
Statistical Analysis of Finite Mixture Distributions.
Lindsay, B. G., Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1987) · Journal of the American Statistical Association · doi:10.2307/2289504 · Cited by 2,905
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
Kitagawa, G. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474692 · Cited by 2,404
Cited by
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Self-citationA Survey of Fault Detection, Isolation, and Reconfiguration Methods
Hwang, I., Kim, S., Kim, Y., & Seah, C. E. (2010) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2009.2026285 · Cited by 1,305
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Murphy, K., & Russell, S. (2001) · Sequential Monte Carlo Methods in Practice · doi:10.1007/978-1-4757-3437-9_24 · Cited by 1,157
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
Cappe, O., Godsill, S. J., & Moulines, E. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.893250 · Cited by 1,009
Particle filter theory and practice with positioning applications
Gustafsson, F. (2010) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2010.5546308 · Cited by 720
Metadata
- Authors
- Doucet, A., Gordon, N. J., & Kroshnamurthy, V.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2001-03
- Volume
- 49
- Issue
- 3
- Pages
- 613-624
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2137345842
- Cited by
- 730
- References
- 27
- FWCI
- 27.04
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Particle filters for state estimation of jump Markov linear systems. https://app.cochat.ai/papers/10.1109/78.905890
- The paper
- Doucet, A., Gordon, N. J., & Kroshnamurthy, V. (2001). Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.905890
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
