A deep learning model based on transformer structure for radar tracking of maneuvering targets

The motion complexity of maneuvering target causes the estimation uncertainty of target motion model, resulting in state estimation error. Especially for strong maneuvering target, the drastic change of target motion models makes the tracking methods hard to adapt and provide accurate state estimation promptly. To solve the state estimation problem of strong maneuvering targets, we propose a new transformer maneuvering target tracking model based on deep learning, named TrMTT model. The TrMTT model uses a new residual mapping between the observation trajectory and the real trajectory to estimate the target states, and is composed of the encoder and decoder branches while the two have the same input of observation trajectory. The encoder extracts the self-attention information for the input at each layer while the decoder implements cross-attention extraction and fusion between features in different layers, thus providing more correlation information between states for learning the transition law of rapidly changing states. Moreover, we propose an input module before the encoder-decoder structure to code the state features of the observation trajectory, and apply two kinds of normalization layers in the input module and the encoder-decoder structure, to project the input into a feature space which facilitates extracting the correlation information between states. Simulation results show that the proposed TrMTT model is superior in performance for maneuvering target tracking compared with other existing approaches.