A GPU-accelerated extended Kalman filter

Shih-Chieh Wei; Bormin Huang

doi:10.1117/12.898552

12 October 2011 A GPU-accelerated extended Kalman filter

Shih-Chieh Wei, Bormin Huang

Proceedings Volume 8183, High-Performance Computing in Remote Sensing; 818304 (2011) https://doi.org/10.1117/12.898552
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

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.

Citation Download Citation

Shih-Chieh Wei and Bormin Huang "A GPU-accelerated extended Kalman filter", Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 818304 (12 October 2011); https://doi.org/10.1117/12.898552

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