Adaptive Dynamic Modelling for Kinematic Positioning

In order to optimize the dynamic model, careful consideration must be undertaken of the dynamics likely to be experienced by the system, while accounting for the measurement rate. The main problems faced by a dynamic model in a Kalman filter occur when the system experiences unexpected dynamic conditions, a change in data acquisition rate, or when in fact the dynamics of the system are non-linear. To minimize the errors produced from dynamic modelling in unusual conditions, a number of strategies can be employed. This paper investigates a range of these strategies that either try to compensate for the error in the dynamic model, or adapt the dynamic model to better suit the new conditions. The results from this study show that a constant acceleration model can be adapted to perform in the same manner as a constant velocity model. Also shown in this paper is that an adaptive variable dimensional dynamic model technique is superior to the others investigated, as it ensures the optimal performance of the Kalman filter and generation of reliable positioning results.