An Extended Dynamic Model for Kinematic Positioning

Abstract

The main problems faced by a dynamic model within a Kalman filter occur when the system experiences unexpected dynamic conditions, a change in data acquisition rate, or when the dynamics of the system are non-linear. To minimize the errors produced from dynamic modeling in unusual conditions, an extended dynamic model is developed in this paper. This paper demonstrates the usefulness of the extended dynamic model through the comparison of performances of a Kalman filter's response to simulated data with a standard dynamic model and the extended dynamic model. The results show that adapting the proposed extended dynamic model is superior to using a standard dynamic model, due mainly to its ability to adapt to a wider range of dynamic conditions, which in turn ensures the optimization of the Kalman filter and the consequent generation of reliable positioning results.

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Cite this paper

@inproceedings{Moore2001AnED, title={An Extended Dynamic Model for Kinematic Positioning}, author={Michael Moore and Jinling Wang}, year={2001} }