# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
###### 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. p 2025 15 2166  
factors, including the solar illumination conditions and the diffuse reflection of the Earth's background, which form \"a complex spatial environment\". Due to the satellite light level constraint imposed by a small volume and the use of a monocular camera with a low power consumption level [7], space object sensing technology has lower payload satellite requirements, but it is more often applied in special on-orbit space environments. Vision-based navigation, which is built on RGB image detection technology, has advantages in terms of its high accuracy and robustness and has recently attracted the attention of several scholars [8; 9; 10; 11]. Most of the existing research has presented different methods or techniques with the same objective of enhancing the performance of PE. The challenge concerning this research problem is related to the lighting environment in space, which is a constraint that affects camera imaging and the relationship between the spatial position information of a satellite and its orientation characteristics. Therefore, some researchers are working on using different kinds of cameras for relative noncooperative space target PE; examples include visual cameras [12], infrared cameras [13], light detection and ranging (LiDAR) sensors [14], and time-of-flight (TOF) cameras [15; 16]. Owing to the limited amount of light in space, active sensors generally have greater robustness than other sensors do. Therefore, LiDAR and TOF cameras have attracted widespread attention from researchers. For example, [PERSON]. [15] presented the projected model of a TOF camera and proposed a new calibration model. The three utilized modules, including a TOF data processing module, a registration module, and a model mapping module, were applied to overcome the above problems with the smallest position and orientation errors. However, vision-based navigation methods formed via the implementation of RGB images have increased in popularity since the development of artificial intelligence. Noncooperative object PE based on optical images can be described as a six-degree-of-freedom (6-DOF) PE problem in computer vision tasks. The research conducted in [17; 18] focused on applying key points for PE.