# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
###### Abstract
Their key-point detection deployment method involves information translation procedures for the object of interest by utilizing perspective-n-points (PnPs) and random sample consensus (RANSAC). Additionally, the PnP and RANSAC frameworks have been implemented [19] to enhance the satellite PE performance achieved by the procedure of a space target landmark regression network, which uses the information derived from the proposed deep 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Ref. [20] demonstrated a binocular PE model based on a self-supervised transformer network (STN) by generating training samples under various conditions and then implementing a combination of the SIFT and a convolutional neural network (CNN) for each generated feature. The feedforward network employed in the transformer of their proposed method was replaced with a global average pooling layer. Another study implemented GoogLeNet in their experiment with some modifications. By deploying a novel loss function, their method achieved better performance than that of the existing approaches [10]. Filterformer-Pose [21] is a PE network that has a CNN as its backbone and feeds its information for mapping a translation and orientation regression network; the authors devised a filter-based transformer encoder model and constructed a hypernetwork-like design based on the self-attention filtering mechanism. This approach has the ability to remove noise and generate adaptive weight information for the utilized deep learning model. [PERSON]. [22] also presented a deep learning approach for satellite PE with the condition of knowing 3D models of the satellite. They proposed a CNN combined with 3D prior knowledge, which was expressed as a 3D model in the form of a point cloud, and used a special loss function that was more suitable for satellite PE tasks to attain desirable performance. Many studies have examined the joint use of two techniques [23; 24; 25; 26; 27], which results in better results every iteration and can improve the final accuracy of the combined approach. The benefit of the iterative technique, which is the first process, is that it can obtain precise results before passing them to the second process. This means that the model parameters can be updated on the fly. In the vision-based learning domain, many papers have discussed input image uncertainty [23; 28; 29; 30; 31]. Researchers have presented different ways to overcome such uncertainty or perform weakly supervised object detection. For the first time, [PERSON]. [23] proposed a joint or cross-task enforcement method for use between weakly supervised object detection and segmentation tasks with a multitask learning scheme; this approach uses the failure patterns of each sub-method to complement the learning process of the other sub-method. [PERSON]. [28; 29; 30] presented a multiple-instance learning (MIL) method with an additional technique to improve the accuracy of weakly supervised detection. Intersecting vectors [31] use binocular vision to reconstruct a straight line and, based on the direction vector and intersection of the straight line, solve the pose of the measured target in the measurement coordinate system, and obtain the initial PE value. The optimization algorithm has been widely used for several approaches. [PERSON]. [32] introduce distributed optimization methods for multiagent systems by using the partial information as an objective function. The result shows the effectiveness of the design. In the context of unmanned aerial vehicle (UAV) research, the gradient-free optimization algorithm has been implemented for the cooperative source-seeking problem using a group of quadrotors equipped with sensors for source scalar field measurement with limited communications [33]. Particle swarm optimization (PSO) was introduced in 1995 by [PERSON] and [PERSON] [34]. It has been implemented in several fields of study, including PE. Ref.