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The present study primarily discusses the perturbance error indeterminacy that is caused by anisotropic and correlated non-identical gray distribution of feature points in vision measurement for space target pose parameters. On that basis, an expedited algorithm that involves perturbance affine term based on the novel statistical objective function is proposed. By invoking the inverse covariance matrix to model a novel data space, the pose estimation algorithm based on projection vector is capable of reducing the effect of different levels of disturbance error on the measured results, as well as effectively avoiding the poor or non-convergence attributed to data degradation. Furthermore, the repeated calculation is avoided by coupling each iteration, which significantly simplifies the computation. As a consequence, the calculation complexity of each iteration decreases from <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O$ </tex-math></inline-formula>(1), and the expediting process is implemented significantly. Lastly, as revealed from the experimental results, the calculation efficiency is improved by 3.3 times, and the maximum measured error of the space target attitude is less than 0.1&#x00B0;. Compared with the conventional methods, the proposed algorithm exhibits the effectively promoted speed-ability, precision and indeterminacy attenuation performance, suggesting that the proposed approach should have promising practical applications in deep-space target capture.

Expedited Pose Estimation Algorithm Involving Perturbance Affine Term Based on Projection Vector for Space Target