Abstract
In an integrated kinematic system, the Kalman filter is commonly used to integrate the data from different sensors (such as GPS/GLONASS and INS) for precise positioning. Reliable Kalman filtering results rely heavily on the correct definition of both the mathematical and stochastic models used in the filtering process: Whilst the mathematical models for various positioning measurements are (sufficiently) known and well documented in the current literature, stochastic modelling is not trivial, in particular for real-time applications. In this paper, a newly developed adaptive Kalman filter algorithm is introduced to directly estimate the variance and covariance components for the measurements. Example applications of the proposed algorithm in GPS/GLONASS kinematic positioning and GPS/INS integration are discussed using test data sets. Test results show that the proposed algorithm can improve the performance of the filtering process.
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Wang, J., Stewart, M.P., Tsakiri, M. (2000). Adaptive Kalman filtering for integration of GPS with GLONASS and INS. In: Schwarz, KP. (eds) Geodesy Beyond 2000. International Association of Geodesy Symposia, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59742-8_53
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DOI: https://doi.org/10.1007/978-3-642-59742-8_53
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