Simo Särkkä
Senior Researcher, Dr.Tech.
- Postal Address:
- BECS/Centre of Excellence
- P.O.Box 12200
- FIN-00076 AALTO,
FINLAND
- Street Address:
-
Room F305, F-Talo, 3rd Floor, Rakentajanaukio 2, Espoo, Finland
- Email:
-
ssarkka@lce.hut.fi
Research Activities
- Non-Linear Kalman Filtering and Smoothing
- Stochastic Differential and Partial Differential Equations
- Particle Filtering and Sequential Monte Carlo Methods
- Signal Processing in Biomedical Applications
Journal Articles
- Simo Särkkä (2010). Continuous-Time and
Continuous-Discrete-Time Unscented Rauch-Tung-Striebel Smoothers.
Signal
Processing, Volume 90, Issue 1, Pages 225-235.
http://dx.doi.org/10.1016/j.sigpro.2009.06.012. (Preprint
as PDF)
- Simo Särkkä and Aapo Nummenmaa (2009). Recursive Noise Adaptive
Kalman Filtering by Variational Bayesian Approximations. IEEE
Transactions on Automatic Control, Volume 54, Issue 3, Pages 596-600.
http://dx.doi.org/10.1109/TAC.2008.2008348.
(Preprint
as PDF)
- Simo Särkkä and Tommi Sottinen (2008). Application of Girsanov Theorem to
Particle Filtering of Discretely Observed Continuous-Time Non-Linear
Systems.
Bayesian Analysis, Volume 3,
Number 03, Pages 555-584.
http://dx.doi.org/10.1214/08-BA322.
- Simo Särkkä (2008). Unscented Rauch-Tung-Striebel
Smoother. IEEE
Transactions on Automatic Control, Volume 53, Issue 3, Pages
845-849.
http://dx.doi.org/10.1109/TAC.2008.919531.
(Preprint
as PDF)
- Simo Särkkä, Aki Vehtari, and Jouko Lampinen (2007).
Rao-Blackwellized Particle Filter for Multiple Target Tracking.
Information Fusion Journal, Volume 8, Issue 1, Pages 2-15. http://dx.doi.org/10.1016/j.inffus.2005.09.009.
(Preprint
as PDF)
- Simo Särkkä, Aki Vehtari, and Jouko Lampinen
(2007). CATS Benchmark Time Series Prediction by Kalman Smoother with
Cross-Validated Noise Density.
Neurocomputing, Volume 70, Issues 13-15, Pages 2331-2341.
http://dx.doi.org/10.1016/j.neucom.2005.12.132 (Preprint
as PDF)
- Simo Särkkä (2007). On Unscented Kalman Filtering for
State Estimation of Continuous-Time Nonlinear Systems. IEEE
Transactions on Automatic Control, Volume 52, Issue 9, Pages
1631-1641.
http://dx.doi.org/10.1109/TAC.2007.904453
(Preprint as
PDF)
- Simo Särkkä and Jouni Hartikainen.
On Gaussian Optimal Smoothing of Non-Linear
State Space Models. Submitted.
The PDF files are draft versions of the journal articles and they are
here to give people an opportunity to check the relevance of the articles
before purchasing the final articles from the publisher.
Please send me an email if you want the latest preprints of the
submitted articles.
Conference Articles
- Simo Särkkä, Aki Vehtari, and Jouko Lampinen (2007).
Prediction of ESTSP Competition Time Series by Unscented Kalman
Filter and RTS Smoother. In Proceedings of ESTSP 2007, Espoo, February 2007.
(Paper as PDF,
Slides as PDF)
- Simo Särkkä (2006). On Sequential Monte Carlo Sampling of
Discretely Observed Stochastic Differential Equations. In
Proceedings of NSSPW,
Cambridge, September 2006. (Paper as PDF, Slides as PDF)
- Simo Särkkä, Aki Vehtari, and Jouko Lampinen (2004). Time
series prediction by Kalman smoother with cross validated noise
density. In Proceedings of
IJCNN 2004,
Budapest, July 2004. The Winner of
Time
Series Prediction Competition - The CATS Benchmark
(Paper as PDF,
Slides as PDF)
- Simo Särkkä, Aki Vehtari, and Jouko Lampinen (2004).
Rao-Blackwellized Monte Carlo data association for multiple target
tracking. In Proceedings of
FUSION 2004
The 7th International Conference on Information Fusion,
Stockholm, June 2004.
(Paper as PDF,
Slides as PDF)
- Aki Vehtari, Simo Särkkä, and Jouko Lampinen (2000). On MCMC
sampling in Bayesian MLP neural networks. In Shun-Ichi Amari,
C. Lee Giles, Marco Gori, and Vincenzo Piuri, editors,
Proceedings of the IJCNN'2000, volume I, pages
317-322, Como, Italy, July 2000. IEEE Computer Society.
(PostScript)
Technical Reports, Theses, Course Material, etc.
- Simo Särkkä (2009). Bayesian Estimation of Time-Varying Systems:
Discrete-Time Systems, Lectures notes of course S-114.4202 held in
Spring 2009 (PDF). The
slides are also available.
- Simo Särkkä (2007). Notes on Quaternions.
(PDF).
- Simo Särkkä and Tommi Sottinen (2007). Application of Girsanov
Theorem to Particle Filtering of Discretely Observed Continuous-Time
Non-Linear Systems. (PDF
in arxiv.org).
- Simo Särkkä (2006). Recursive Bayesian Inference on
Stochastic Differential Equations. Doctoral dissertation.
http://lib.tkk.fi/Diss/2006/isbn9512281279/isbn9512281279.pdf.
- Simo Särkkä, Toni Tamminen, Aki Vehtari, and Jouko
Lampinen (2004). Probabilistic methods in multiple target tracking -
Review and bibliography. Published as technical report B36, ISBN
951-22-6938-4, Helsinki University of Technology. Laboratory of
Computational Engineering, 2004. (PDF)
-
WO/2004/111677,
WO/2008/034944,
WO/2006/108921
Software Packages
Some Matlab toolboxes that I have contributed to:
Posters
- Simo Särkkä, Aki Vehtari, Jouko Lampinen.
Rao-Blackwellized Particle Filter for Tracking Unknown Number of
Targets in Clutter. Poster in the Fourth Workshop on Bayesian
Inference in Stochastic Processes, June 2005.
(A4 Poster as PDF)
Finnish Material
- MCMC-menetelmät ja diagnostiikat (erikoistyö)
(HTML) (ps.gz)
- Bayesilaiset menetelmät audiovisuaalisen puheen havaitsemisen
mallintamisessa (dippatyö), December 2000 (ps.gz)
- My finnish home page
Teaching
Projects
Links to Material Related to Optimal Filtering