A deep learning-based maneuvering target tracking with temporal convolutional networks

Abstract

Traditional algorithms of the target tracking rely on predefined target motion states to modify sensor observations. However, these algorithms struggle to accurately and promptly model the maneuvering state of a target, thereby failing to provide precise state estimation when the target exhibits maneuvering behavior. To address this challenge, we propose a maneuvering target tracking algorithm based on temporal convolutional networks (TcnMTT). The TcnMTT model employs a constant velocity model-based unscented Kalman filter to decompose the input trajectory into high maneuver state and low maneuver state. Furthermore, the model directly maps the input observations to the true trajectory through a set of symmetric TCN networks. Additionally, TcnMTT incorporates an instance normalization module to project features into a specific feature space and combines a channel attention mechanism to extract feature correlations. Simulation results demonstrate that the proposed TcnMTT model outperforms existing methods in tracking maneuvering targets.