DeepGTT: A general trajectory tracking deep learning algorithm based on dynamic law learning

Abstract

Deep learning technology provides novel solutions for increasingly complex target tracking requirements. For traditional target tracking models, the movement of the target need to be simulated by a predefined mathematical model. However, it is extremely difficult to obtain sufficient information in advance, which makes it challenging to track changeable and noisy trajectories in a timely and precise manner. A deep learning framework is constructed for automatic trajectory tracking based on learning the dynamic laws of motion, called DeepGTT. Specifically, a trajectory generator and a trajectory mapper were designed to standardise trajectory data and construct trajectory mapping, which enable the long short-term memory–based tracking network to learn general dynamic laws. Then, to discuss the interpretability of the model, the mechanism of the deep learning framework is considered and a memory factor matrix is defined. Finally, extensive experiments are conducted on various weak manoeuvring and manoeuvring scenarios to evaluate the algorithm. Experimental results demonstrate that the DeepGTT algorithm remarkably improves accuracy and efficiency compared with most conventional algorithms and state-of-the-art methods. In addition, interpretability experiments qualitatively prove that the tracking network can perceive dynamic laws when estimating the target state.