Adaptive multi-maneuver target model tracking method based on digital images

This is the authors' abstract. We don't add key points for this paper.

The primary approach to analyzing the model of a maneuvering target is to treat the target's maneuvers as an increase in the state noise variance. Within this context, the Singer model and the "current" statistical model are extensively employed, while the Jerk model is recommended for targets with high agility. This study conducts simulation tests on three frequently utilized models for maneuvering targets. It finds that the Singer model is apt for describing targets moving at a steady speed or with uniform acceleration, whereas the "current" statistical model is particularly effective for modeling target movements involving step changes in acceleration, especially in comparison to the Jerk model. The outcomes of these simulation tests reveal that the proposed method achieves a root mean square error (RMSE) of 3.92 when transitioning from uniform acceleration to step-acceleration maneuvers. This RMSE is significantly lower than those of 6.23 and 4.78, indicating enhanced tracking precision.