###### Abstract
[20] introduced a feature point selection method based on Neighborhood Feature Variance (NFV) to remove redundant points and retain more significant points for fine alignment, thereby enhancing the accuracy of attitude estimation for spatial targets. A limitation of this method is that the accuracy of key point extraction is constrained by the determination of the radius of the NFV neighborhood. It is evident that effectively filtering out redundant point noise in the pose estimation process significantly influences the accuracy of pose estimation. In order to enhance the accuracy of point cloud alignment and minimize computational effort, this study introduces a novel point cloud-based approach for estimating the attitude of non-cooperative targets in close-range operations. Existing methods for point cloud alignment and attitude estimation often rely on model-based approaches or specific target components. For example, traditional methods like the ICP [21] algorithm and its variants such as Generalized Iterative Closest Point (GICP) [22] are widely used but have notable limitations. One significant drawback of the ICP algorithm is its sensitivity to initial alignment and noise, which can lead to suboptimal results, especially with high noise levels and outliers. Variants like GICP attempt to address some of these issues but still struggle with complex geometries and high-speed rotational targets. Additionally, methods relying on typical target components (e.g., nozzles and fairings) or pre-acquired models can be limited by their dependence on accurate prior knowledge, which may not always be available or reliable in all scenarios. To overcome these limitations, our approach employs a moving least squares (MLS)-based microplanar projection technique. This technique transforms the original point cloud into a series of smooth manifold surfaces, effectively filtering out redundant and noisy points while preserving the geometric characteristics of the point cloud surface. By doing so, it enhances the accuracy of point cloud alignment and reduces computational effort. Furthermore, we introduce the Joint Iterative Closest Point (J-ICP) algorithm, which mitigates cumulative errors and achieves robust alignment of point clouds from satellite targets, addressing the issues of noise sensitivity and initial alignment dependency found in traditional methods. The proposed method not only delivers significantly superior estimation results compared to traditional alignment algorithms but also operates efficiently without relying on extensive prior models or specific target features. This makes it particularly suitable for close-range operations involving non-cooperative targets, where quick and accurate attitude estimation is critical. In summary, our main contributions are:  
Introduction of the microplanar projection-based moving least squares (mpp-MLS) preprocessing method: This method enhances point cloud alignment accuracy by removing redundant and noisy points while preserving the geometric characteristics of the point cloud surface. Development of an improved J-ICP algorithm: This algorithm precisely aligns point clouds by considering small angle transformations over multiple time points, thus avoiding local optimization issues commonly encountered in single-frame data alignment. Validation through semi-physical experiments: The proposed method has been validated through semi-physical experiments, demonstrating significant reductions in maximum translational and rotational errors compared to classical methods, with only a slight increase in computational overhead. These results highlight the method's potential for practical applications in space mission control. The subsequent sections of this paper are structured as follows: Section 2 elaborates on the handling of redundant points and the alignment method. Section 3 presents the validation of the proposed method through semi-physical experiments conducted on simulated spatial targets. The results of these experiments showcase the method's effectiveness and robustness.