Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution

Robert Piche; Simo Särkkä; Jouni Hartikainen

doi:10.1109/MLSP.2012.6349794

Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution

Robert Piche
, Simo Särkkä
, Jouni Hartikainen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

188 Citations (Scopus)

284 Downloads (Pure)

Abstract

Nonlinear Kalman filter and Rauch–Tung–Striebel smoother type recursive estimators for nonlinear discrete-time state space models with multivariate Student’s t-distributed measurement noise are presented. The methods approximate the posterior state at each time step using the variational Bayes method. The nonlinearities in the dynamic and measurement models are handled using the nonlinear Gaussian filtering and smoothing approach, which encompasses many known nonlinear Kalman-type filters. The method is compared to alternative methods in a computer simulation.

Translated title of the contribution	Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution
Original language	English
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-4673-1025-3
ISBN (Print)	978-1-4673-1024-6
DOIs	https://doi.org/10.1109/MLSP.2012.6349794
Publication status	Published - 2012
Publication type	A4 Article in conference proceedings

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)	1551-2541

Publication forum classification

Publication forum level 1

Access to Document

10.1109/MLSP.2012.6349794

Piche recursive outlier robust filtering
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

http://urn.fi/URN:NBN:fi:tty-201311051415

Cite this

Piche, R., Särkkä, S., & Hartikainen, J. (2012). Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain (pp. 1-6). Article 13117229 (IEEE International Workshop on Machine Learning for Signal Processing). IEEE. https://doi.org/10.1109/MLSP.2012.6349794

Piche, Robert ; Särkkä, Simo ; Hartikainen, Jouni. / Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution. 2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain. Piscataway, NJ : IEEE, 2012. pp. 1-6 (IEEE International Workshop on Machine Learning for Signal Processing).

@inproceedings{aa7880c610354066a9815ce8f083f787,

title = "Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution",

abstract = "Nonlinear Kalman filter and Rauch–Tung–Striebel smoother type recursive estimators for nonlinear discrete-time state space models with multivariate Student{\textquoteright}s t-distributed measurement noise are presented. The methods approximate the posterior state at each time step using the variational Bayes method. The nonlinearities in the dynamic and measurement models are handled using the nonlinear Gaussian filtering and smoothing approach, which encompasses many known nonlinear Kalman-type filters. The method is compared to alternative methods in a computer simulation.",

author = "Robert Piche and Simo S{\"a}rkk{\"a} and Jouni Hartikainen",

note = "Page numbers will be assigned later.<br/>Contribution: organisation=mat,FACT1=1<br/>Publisher name: IEEE",

year = "2012",

doi = "10.1109/MLSP.2012.6349794",

language = "English",

isbn = "978-1-4673-1024-6",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

pages = "1--6",

booktitle = "2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain",

address = "United States",

}

Piche, R, Särkkä, S & Hartikainen, J 2012, Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution. in 2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain., 13117229, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, Piscataway, NJ, pp. 1-6. https://doi.org/10.1109/MLSP.2012.6349794

Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution. / Piche, Robert; Särkkä, Simo; Hartikainen, Jouni.
2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain. Piscataway, NJ: IEEE, 2012. p. 1-6 13117229 (IEEE International Workshop on Machine Learning for Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution

AU - Piche, Robert

AU - Särkkä, Simo

AU - Hartikainen, Jouni

N1 - Page numbers will be assigned later.<br/>Contribution: organisation=mat,FACT1=1<br/>Publisher name: IEEE

PY - 2012

Y1 - 2012

N2 - Nonlinear Kalman filter and Rauch–Tung–Striebel smoother type recursive estimators for nonlinear discrete-time state space models with multivariate Student’s t-distributed measurement noise are presented. The methods approximate the posterior state at each time step using the variational Bayes method. The nonlinearities in the dynamic and measurement models are handled using the nonlinear Gaussian filtering and smoothing approach, which encompasses many known nonlinear Kalman-type filters. The method is compared to alternative methods in a computer simulation.

AB - Nonlinear Kalman filter and Rauch–Tung–Striebel smoother type recursive estimators for nonlinear discrete-time state space models with multivariate Student’s t-distributed measurement noise are presented. The methods approximate the posterior state at each time step using the variational Bayes method. The nonlinearities in the dynamic and measurement models are handled using the nonlinear Gaussian filtering and smoothing approach, which encompasses many known nonlinear Kalman-type filters. The method is compared to alternative methods in a computer simulation.

U2 - 10.1109/MLSP.2012.6349794

DO - 10.1109/MLSP.2012.6349794

M3 - Conference contribution

SN - 978-1-4673-1024-6

T3 - IEEE International Workshop on Machine Learning for Signal Processing

SP - 1

EP - 6

BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain

PB - IEEE

CY - Piscataway, NJ

ER -

Piche R, Särkkä S, Hartikainen J. Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain. Piscataway, NJ: IEEE. 2012. p. 1-6. 13117229. (IEEE International Workshop on Machine Learning for Signal Processing). doi: 10.1109/MLSP.2012.6349794

Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution

Abstract

Publication series

Publication forum classification

Access to Document

Fingerprint

Cite this