# Generalized Kalman smoothing: Modeling and algorithms

@article{Aravkin2017GeneralizedKS, title={Generalized Kalman smoothing: Modeling and algorithms}, author={Aleksandr Y. Aravkin and James V. Burke and Lennart Ljung and Aur{\'e}lie C. Lozano and Gianluigi Pillonetto}, journal={Autom.}, year={2017}, volume={86}, pages={63-86} }

State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch-Tung-Striebel and Mayne-Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a convex quadratic objective function with structure induced by the dynamic model.
These classical formulations fall short in many important circumstances. For instance, smoothers… Expand

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