A deep learning algorithm for joint direct tracking and classification of manoeuvring sources

The remarkable success of deep learning technologies has provided new ideas for solving complex tracking problems. It is difficult for traditional algorithms to directly estimate the trajectory vector and target class from the received signal due to the limitation of modelling ability, which causes inevitable information loss. Moreover, existing algorithms suffer severe performance degradation when dealing with problems that are difficult to mathematically model in advance, such as highly nonlinear observations and manoeuvring scenarios. To address these issues, we propose a deep learning algorithm for joint direct tracking and classification (DeepDTC), which is a novel direct tracking framework. Specifically, we construct a convolutional neural network (CNN)-based signal processing component to capture observation features, and a Transformer-based trajectory tracking component to capture the features of target state and identity. Meanwhile, in signal processing component, we design an attribute network to learn auxiliary knowledge features. Finally, we construct a multi-task learning network to connect these components and estimate trajectory vectors and target classes simultaneously. Our algorithm takes the transformer with attention mechanism as the core network, which is highly scalable and suitable for introducing various auxiliary knowledge. The comparison experiments with traditional methods demonstrate the effectiveness and advancement of the proposed algorithm. The comparative experiments with the single-task model show that DeepDTC can further improve the tracking accuracy by utilising the learnt classification information.