Juho Kokkala

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We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The(More)
We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare(More)
We consider approximate maximum likelihood parameter estimation in nonlinear state-space models. We discuss both direct optimization of the likelihood and expectation– maximization (EM). For EM, we also give closed-form expressions for the maximization step in a class of models that are linear in parameters and have additive noise. To obtain approximations(More)
This paper is concerned with the use of split-Gaussian importance distributions in sequential importance resampling based particle filtering. We present novel particle filtering algorithms using the split-Gaussian importance distributions and compare their performance with several alternatives. Using a univariate nonlinear reference model, we compare the(More)
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