Data-Driven Intelligent Multiframe Joint Tracking Method for Maneuvering Targets in Clutter Environments

Classic maneuvering targets tracking (MTT) algorithms require accurate kinematic models, the performance of which would be degraded significantly for the intricate and abrupt maneuvers. In addition, their sensitivity to maneuverability would be reduced in the presence of clutters. In this article, we consider the MTT problem without using preset kinematic models in the clutter environments, and propose a data-driven intelligent multiframe joint tracking method (DIMJTM) for maneuvering targets with clutters. Initially, we introduce a maneuverability characterization method using modified Mahalanobis distance. Based on this, a backtracking vertex sampling strategy is proposed for selecting multiframe historical track points matching the current maneuver pattern. Subsequently, we propose a pyramid shaped graph construction scheme to establish multiple spatio–temporal relationships among the selected track points and measurements of the current frame. Finally, the composed graph is fed into our designed interactive Transformer-graph attention network autoencoder. The simulated and real experimental results indicate that the DIMJTM not only accurately captures maneuverability changes, but also outperforms the mainstream interacting multiple model probabilistic data association (IMM-PDA)/integrated PDA algorithms and their improved variants in the single-target tracking scenarios and the multiple hypothesis tracking (MHT)/IMM-MHT algorithms in the multiple targets tracking scenario, achieving stable and high-precision tracking.