VLSTMMTT: a maneuvering target-tracking algorithm based on variational LSTM

Traditional maneuvering target-tracking(MTT) algorithms typically require predefined mathematical motion models to simulate the target's motion. Due to the difficulty in precisely predicting the mathematical model of the target in practice, traditional algorithms often suffer from tracking delays and large trajectory errors. Data-driven deep learning methods do not rely on predefined mathematical models, bringing new ideas to MTT. However, these methods often lack the ability to generalize the randomness of target motion. To address this issue, a Variational LSTM based MTT algorithm VlstmMTT is proposed. After learning a large amount of offline maneuvering trajectory data, the algorithm has the capability to encode and decode the motions of maneuvering targets. Simulation results show that VlstmMTT can achieve faster and more accurate tracking of maneuvering targets in different scenarios.