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arXiv · 1206.4670

State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction

Jouni Hartikainen·Mari Seppanen·Simo Sarkka
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Abstract

Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with non-parametric data-driven components. Several key applications of LFMs need non-linearities, which results in analytically intractable inference. In this work we show how non-linear LFMs can be represented as non-linear white noise driven state-space models and present an efficient non-linear Kalman filtering and smoothing based method for approximate state and parameter inference. We illustrate the performance of the proposed methodology via two simulated examples, and apply it to a real-world problem of long-term prediction of GPS satellite orbits.

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Jouni Hartikainen, Mari Seppanen, Simo Sarkka. 2012-06-18. State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. https://arxiv.org/abs/1206.4670

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