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Paper TuA201.5

Lau, Tak Kit (The Chinese University of Hong Kong), Liu, Yunhui (Chinese University of Hong Kong), Lin, Kai Wun (The Chinese University of Hong Kong)

Evolutionary Tuning of Sigma-Point Kalman Filters

Scheduled for presentation during the Regular Sessions "Aerial Robotics II" (TuA201), Tuesday, May 10, 2011, 11:05−11:20, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 21, 2019

Keywords Aerial Robotics, Field Robots

Abstract

The Kalman filter is widely used but the tedious and time-consuming tunings of the filter parameters must be unavoidably carried out before use, and frustratingly, after every reconfiguration on the sensors. In this paper, we formulated the measurement residual in a performance index, and utilised an evolutionary method to automatically and efficiently calibrate the parameters of the sigma-point Kalman filter. Without analytically resolving the nonlinear and multivariate process and measurement models in the filter, the proposed method implicitly solves for the filter parameters in a gradient-free manner through a series of strategies including the selection, crossover and shuffling mutation. Furthermore, to demonstrate the superior performance of the method, we applied this method to a highly nonlinear and coupled state estimation problem on an unmanned helicopter which experiences a GNSS outage. The empirical results showed that the proposed method not only automated and significantly accelerated the exhausting tweaking of the filter parameters, but also yielded a high quality tuning result that strikingly outperformed an earlier, painstakingly handcrafted calibration.

 

 

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