GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters

 

D.J. Jwo*¹, F.C. Chung², K.L. Yu³

 

¹ Department of Communications, Navigation and Control Engineering National Taiwan Ocean University, Keelung 20224, Taiwan. *djjwo@mail.ntou.edu.tw.

² Inventec Appliances, Wugu Industrial Park Wugu, New Taipei City 24890, Taiwan.

³ Systems & Technology Corp., Hsichih, New Taipei City 22101, Taiwan.

 

ABSTRACT

In this paper, performance evaluation for various single model nonlinear filters and nonlinear filters with interacting multiple model (IMM) framework is carried out. A high gain (high bandwidth) filter is needed to response fast enough to the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniform motion periods. Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamic models just when required, diminishing unrealistic noise considerations in non-maneuvering situations. The IMM estimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matched to different motion modes of the platform. The use of an IMM allows exploiting the benefits of high dynamic models in the problem of vehicle navigation. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

Keywords: Interacting multiple model, Unscented Kalman filter, Unscented particle filter, Integrated navigation.

 

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Acknowledgements

Funding for this work was provided by the National Science Council of the Republic of China under grant numbers NSC 97-2221-E-019-012 and NSC 98-2221-E019-021-MY3.

 

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