Multiple Model Kalman Filtering for GPS and Low-cost INS Integration

Christopher Hide, Terry Moore and Martin Smith

Abstract:	GPS and Inertial Navigation Systems are used for positioning and attitude determination in a wide range of applications. The high cost of inertial components is the primary limitation of the proliferation of such technology into a broader range of applications. Current low cost inertial sensors exhibit large errors which require constant calibration using GPS measurements. GPS and INS measurements are typically combined using a Kalman filter. The conventional Kalman filtering algorithm requires the definition of a dynamic and stochastic model. The dynamic model describes how the errors that are being modelled develop over time, whereas the stochastic model provides information about the noise of the sensors. These matrices are typically defined using a priori information obtained from the sensor specification provided by the manufacturer. However, particularly for low cost sensors, the definition of a priori statistics is unlikely to provide the best performance since the sensor errors are likely to vary temporally. In order to achieve the highest performance from the Kalman filter algorithm, it is desirable to estimate the process noise matrix for the INS on-line. This paper examines the use of Multiple Model Adaptive Estimation (MMAE) where multiple Kalman filters are run in parallel using different dynamic or stochastic models. The MMAE algorithm is used to select either a single ’best’ Kalman filter solution, or the algorithm can be used to combine the output from all of the Kalman filters into a single combined solution. The obvious limitation of such an approach is the large computational burden imposed by running multiple Kalman filters. However, with improved processor technology, such an approach can now be considered even for real-time applications. This paper demonstrates that the MMAE algorithm can provide a reduction in the time required to initially align the INS. The MMAE algorithm can also provide a reduction in the attitude errors experienced during typical navigation conditions.
Published in:	Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004) September 21 - 24, 2004 Long Beach Convention Center Long Beach, CA
Pages:	1096 - 1103
Cite this article:	Hide, Christopher, Moore, Terry, Smith, Martin, "Multiple Model Kalman Filtering for GPS and Low-cost INS Integration," Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004), Long Beach, CA, September 2004, pp. 1096-1103.
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