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Anti-interference aerial target tracking for infrared seeker via spatiotemporal correlation of topological graph networks

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Abstract

The bottleneck of artificial interference in the background of complex air combat is the major obstacle in the precise detection and guidance of infrared air-to-air missiles. Occlusion and similarity of infrared point-source interference to aerial targets pose a serious challenge to the anti-interference tracking performance of an infrared imaging seeker. Based on the analysis of relative motion of the target and interference and the spatial topological structure of multi-object in the image plane, we propose an anti-interference tracking algorithm based on the spatiotemporal correlation of an image plane topological graph network. Through infrared image preprocessing and connected-region labeling, the regions of the image plane are automatically and accurately extracted to achieve accurate detection of suspected targets. The topological graph network of the image plane multi-objects is established by using the geometric topological relation of the connected regions, spatial direction relation, and spatial distance relation. Using the anti-interference tracking strategy of the spatiotemporal correlation of the topological graph network, the topological graph of the current frame is registered according to its spatial topological relationship; further, automatic acquisition of the target under a strong interference situation is realized. The optimization of the parameters effectively solves the problem of searching and matching suspected target nodes in the graph alignment. One-pass evaluation experiments show high tracking accuracy and speed of 90.0% and 248.6 FPS, respectively.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62273279, 61703337) and Aviation Science Foundation of China (ASFC-20191053002).

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Correspondence to Shaoyi Li.

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Yang, X., Li, S., Zhang, X. et al. Anti-interference aerial target tracking for infrared seeker via spatiotemporal correlation of topological graph networks. J Opt 52, 510–519 (2023). https://doi.org/10.1007/s12596-022-01038-0

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