Multiscale Estimation of Terrain Complexity Using ALSM Point Data on Variable Resolution Grids

机译：使用可变分辨率网格上的ALSM点数据进行地形复杂度的多尺度估计

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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.

机译：先前已将多尺度卡尔曼平滑器（MKS）用于数据融合应用和地形估计。但是，当估计非平稳地形变化时，尤其是当地形突然变化时，嵌入单个随机模型的标准MKS算法会提供次优性能。在这项工作中，多个MKS模型由专家混合（MOE）网络进行调节，以自适应地融合估计值。尽管MOE已被广泛应用于一维时间序列数据，但是它扩展到多尺度估计是新的。

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来源
《Gravity, Geoid and Space Missions: GGSM 2004; International Association of Geodesy Symposia; vol.129》|2004年|P.224-229|共6页
会议地点 Porto(PT)
作者
K.C. Slatton; K. Nagarajan; V. Aggarwal; H. Lee; W. Carter; R. Shrestha;
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Department of Electrical and Computer Engineering University of Florida;

PO Box 116130;

Gainesville, FL 32611;

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原文格式 PDF
正文语种 eng
中图分类大地测量学;
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Multiscale Estimation of Terrain Complexity Using ALSM Point Data on Variable Resolution Grids

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