Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions - Guljaina Kazezkhan; Na Wang; Qian Xu; Shangmin Lin; Hui Wang; Fei Xue; Feilong He; Xiaoman Cao (Machines, 2025)
Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may degrade when limited pose diversity leads to poor parameter excitation. To address these challenges, this paper proposes a novel robust self-calibration framework that integrates Huber loss and <semantics>L2</semantics> regularization into the Levenberg–Marquardt (LM) algorithm—yielding a hybrid optimization approach that combines residual robustness, numerical stability, and convergence reliability. A comprehensive simulation study was conducted under varying workspace sizes and sensor noise levels. The proposed method maintained stable performance even under reduced excitation and high-noise conditions, where traditional LM methods typically degrade, confirming its robustness and applicability to realistic calibration scenarios. The framework was further validated using a structured-light 6-DOF pose measurement system, the proposed method achieved over 90% improvement in both position and orientation accuracy compared to the traditional LM approach. These findings confirm the method’s effectiveness for high-precision 6-DOF calibration in parallel mechanisms, and its suitability for real-world applications in radio telescope subreflector alignment.