A multi-directional motion interacting fusion model for diver tracking
Keywords:
moving diver, multi-direction motion model, time-varying transition probability, interacting fusion, parallel Kalman filter,Abstract
According to the diver motion characteristics,
which are low speed and rapid change of direction,
a multi-directional motion model is presented.
Then the motion model is introduced into an
interacting multiple model method, while the timevarying
motion model transition probability was
corrected according to current measurements.
Firstly, the predictive state was obtained by a
multi-directional motion model. Secondly, the
parallel Kalman filters were applied to estimate
multi-directional state. Finally, the interactive
fusion processing for estimations from multidirectional
motion model was conducted to
implement diver state estimation. The method was
verified by both simulation and experiment. The
results show that the proposed method has higher
tracking accuracy and superior adaptability than
conventional interactive multiple model algorithm
based on single direction motion model. The
proposed method is effective for diver tracking.
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