• Publications
  • Influence
Bayesian Filtering and Smoothing
  • S. Särkkä
  • Computer Science
  • Institute of Mathematical Statistics textbooks
  • 2013
TLDR
Sarkka presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Expand
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
TLDR
This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. Expand
Rao-Blackwellized particle filter for multiple target tracking
TLDR
We propose a new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets. Expand
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
  • S. Särkkä
  • Mathematics, Computer Science
  • IEEE Trans. Autom. Control.
  • 17 September 2007
TLDR
This paper considers the application of the unscented Kalman filter (UKF) to continuous-time filtering problems, where both the state and measurement processes are modeled as stochastic differential equations. Expand
Optimal Filtering with Kalman Filters and Smoothers
TLDR
In this paper we present a documentation for an optimal filtering toolbox for discrete-time state space models, including the well-known linear Kalman filter and several non-linear extensions to it. Expand
Recursive Bayesian inference on stochastic differential equations
TLDR
This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. Expand
Applied Stochastic Differential Equations
TLDR
The topic of this book is stochastic differential equations (SDEs). Expand
Unscented Rauch-Tung-Striebel Smoother
  • S. Särkkä
  • Mathematics, Computer Science
  • IEEE Trans. Autom. Control.
  • 8 April 2008
TLDR
A new Rauch-Tung-Striebel type form of the fixed-interval unscented Kalman smoother is derived for optimal smoothing of state-space models. Expand
Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
TLDR
We show that continuous-time prior can be defined by any linear, time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our trajectory estimates using a variety of vehicle dynamics models. Expand
Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution
TLDR
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. Expand
...
1
2
3
4
5
...