Abstract:

For aerodynamic targets with high maneuvering characteristics such as unmanned aerial vehicles, traditional target tracking algorithms based on fixed motion models often suffer from model mismatch when dealing with complex motion patterns. To address this issue, a multi-model interaction tracking algorithm is proposed based on adaptive coordinated turn （ACT） model and bidirectional long short-term memory （Bi-LSTM） network. The algorithm uses Bi-LSTM network to learn the nonlinear relationship between historical observation data and target motion state, and realizes the accurate identification of target turning rate. Based on the idea of hybrid data-model driven, the prediction results of neural network are combined with the interacting multiple model-ACT （IMM-ACT） filtering algorithm. It effectively improves the response ability of the algorithm when the target motion mode switches. The experimental results show that the proposed algorithm has higher tracking accuracy in the scenarios of target motion pattern diversification and high-frequency switching, which significantly improves the stability and continuity of maneuvering target tracking, and provides a solution to maneuvering target tracking.

Key words: maneuvering target tracking, bidirectional long short-term memory （Bi-LSTM） network, adaptive coordinated turn （ACT） model, interacting multiple model algorithm （IMM）