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Proceedings Paper

A GPU-accelerated extended Kalman filter
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Paper Abstract

The extended Kalman filter is one of the most widely used techniques for state estimation of nonlinear systems. In its two steps of forecast and data assimilation, many matrix operations including multiplication and inversion are involved. As recent graphic processor units (GPU) have shown to provide much speedup in matrix operations, we will explore in this work a GPU-based implementation of the extended Kalman filter. The Compute Unified Device Architecture (CUDA) on the Nvidia GeForce GTX 590 GPU hardware will be used for comparison with a single threaded CPU counterpart. Experiments were conducted on typical large-scale over-determined systems with thousands of components in states and measurements. Within the GPU memory limit, a speedup of 1386x is achieved for a system with measurements having 5000 components and states having 3750 components. The speedup profile for various combinations of measurement and state sizes will serve as good reference for future implementation of extended Kalman filter on real large-scale applications.

Paper Details

Date Published: 12 October 2011
PDF: 8 pages
Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 818304 (12 October 2011); doi: 10.1117/12.898552
Show Author Affiliations
Shih-Chieh Wei, Tamkang Univ. (Taiwan)
Bormin Huang, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 8183:
High-Performance Computing in Remote Sensing
Bormin Huang; Antonio J. Plaza, Editor(s)

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