Abstract
Multiscale 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.
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Slatton, K., Nagarajan, K., Aggarwal, V., Lee, H., Carter, W., Shrestha, R. (2005). Multiscale Estimation of Terrain Complexity Using ALSM Point Data on Variable Resolution Grids. In: Jekeli, C., Bastos, L., Fernandes, J. (eds) Gravity, Geoid and Space Missions. International Association of Geodesy Symposia, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26932-0_39
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DOI: https://doi.org/10.1007/3-540-26932-0_39
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