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

Gang Liu; Ronghui Wang

doi:10.1117/12.3035343

19 July 2024 Adaptive multi-maneuver target model tracking method based on digital images

Gang Liu, Ronghui Wang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132T (2024) https://doi.org/10.1117/12.3035343
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gang Liu and Ronghui Wang "Adaptive multi-maneuver target model tracking method based on digital images", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132T (19 July 2024); https://doi.org/10.1117/12.3035343

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