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Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method

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In this paper, we propose a fast and reliable track-before-detect approach to simultaneously detect, track, and identify an unknown and variable number of resident space objects (RSOs) without any prior information and any explicit detection, which leads to better space domain awareness. Specifically, we use the point spread function concept to propose a separable likelihood function as the observation model in the random finite set-based multi-Bernoulli filtering framework. This framework clearly distinguishes RSOs from any counterfeit objects and detects and tracks them immediately after their respective appearance in background cluttered telescope imagery data. The extensive experimental results on the TAOS dataset demonstrate the robustness of the proposed method in detecting and tracking RSOs with the average optimal subpattern assignment localization error less than 2 pixels in image sequences with the signal to noise ratio as low as 9 dB and under the conditions of varying illumination and occlusion.

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The authors would like to thank Dr. Kohei Fujimoto for providing the dataset and his constructive suggestions during the planning and development of this research work.

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Correspondence to Mohammadreza Javanmardi.

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Javanmardi, M., Qi, X. Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method. Machine Vision and Applications 29, 1191–1208 (2018).

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