Robust Inference for State-Space Models with Skewed Measurement Noise
2015 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 11, 1898-1902 p.Article in journal (Refereed) Published
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2015. Vol. 22, no 11, 1898-1902 p.
Kalman filter; robust filtering; RTS smoother; skew t; skewness; t-distribution; variational Bayes
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-120129DOI: 10.1109/LSP.2015.2437456ISI: 000356458700003OAI: oai:DiVA.org:liu-120129DiVA: diva2:841662
Funding Agencies|Tampere University of Technology Graduate School; Finnish Doctoral Programme in Computational Sciences (FICS); Foundation of Nokia Corporation; Swedish research council (VR), project ETT [621-2010-4301]2015-07-142015-07-132015-10-05