TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Robust Inference for State-Space Models with Skewed Measurement Noise

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1898-1902
Sivumäärä5
JulkaisuIEEE Signal Processing Letters
Vuosikerta22
Numero11
DOI - pysyväislinkit
TilaJulkaistu - 1 marraskuuta 2015
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.

Latausten tilastot

Ei tietoja saatavilla