Publication: Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
0
0
Issued Date
2025-02-01
Resource Type
eISSN
20763417
Scopus ID
2-s2.0-85218463188
Journal Title
Applied Sciences (Switzerland)
Volume
15
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Sciences (Switzerland) Vol.15 No.4 (2025)
Suggested Citation
Kamsing P., Cao C., Zhao Y., Boonpook W., Tantiparimongkol L., Boonsrimuang P. Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization. Applied Sciences (Switzerland) Vol.15 No.4 (2025). doi:10.3390/app15042166 Retrieved from: https://hdl.handle.net/20.500.14740/20011
Corresponding Author(s)
Other Contributor(s)
Abstract
Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by reducing the need for manual control in the future. This article presents a joint iterative satellite PE and particle swarm optimization (PE-PSO) method. The PE-PSO method uses the number of batches derived from satellite PE as the number of particles and keeps the number of epochs from the satellite PE process as the number of epochs for PSO. The objective function of PSO is the training function of the implemented network. The output obtained from the previous objective function is applied to update the new positions of the particles, which serve as the inputs of the current training function. The PE-PSO method is tested on synthetic Soyuz satellite image datasets acquired from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs) under different preset hyperparameters. The proposed method significantly reduces the incurred loss, especially during the batch-processing operation of each epoch. The results illustrate the accuracy improvement attained by the PE-PSO method over epoch processing, but its time consumption is not distinct from that of the conventional method. In addition, PE-PSO achieves better performance by reducing the mean position estimation error by 13.1% and the mean orientation estimation error on the testing dataset by 29.1% based on the pretrained weights of Common Objects in Context (COCO). Additionally, PE-PSO improves the accuracy of the Soyuz_hard-based weight by 7.8% and 0.3% in terms of the mean position estimation error and mean orientation estimation error, respectively.