Ontology type: schema:Chapter
2005
AUTHORSK.C. Slatton , K. Nagarajan , V. Aggarwal , H. Lee , W. Carter , R. Shrestha
ABSTRACTMultiscale Kalman smoothers (MKS) have been previously employed for data fusion applications and estimation of topography. However, the standard MKS algorithm embedded with a single stochastic model gives suboptimal performance when estimating non-stationary topographic variations, particularly when there are sudden changes in the terrain. In this work, multiple MKS models are regulated by a mixture-of-experts (MOE) network to adaptively fuse the estimates. Though MOE has been widely applied to one-dimensional time series data, its extension to multiscale estimation is new. More... »
PAGES224-229
Gravity, Geoid and Space Missions
ISBN3-540-26930-4
http://scigraph.springernature.com/pub.10.1007/3-540-26932-0_39
DOIhttp://dx.doi.org/10.1007/3-540-26932-0_39
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