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A self-calibration algorithm for satellite sensors based on vector observations

机译：基于向量观测的卫星传感器自校准算法

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The advance of space technology allowed small satellites to accomplish missions that were once only possible with big and expensive platforms. The quality and accuracy of small sensors have also improved, leading to a better knowledge of the spacecraft attitude. However, the integration and assembly process of such platforms has constraints that often hinder a high accuracy placement and calibration of the equipment. This translates into the three most common errors in sensor measurements: bias, misalignment, and non-orthogonality. This work proposes a new algorithm designed to estimate and correct those three error sources for any sensor based on vector observations. The algorithm is based on the same principle used by inertial navigation systems with non-inertial information. A propagator computes the attitude based on the gyro readings with the initial estimation provided by the other sensors. Concurrently, a Kalman filter estimates the attitude and sensor errors. After filter convergence, the estimation is used to correct the attitude knowledge. An observability analysis is carried out, showing in which conditions the filter can correctly estimate the error state. Afterward, the proposed technique is tested, employing a Monte Carlo simulation in a validated satellite simulator. The results show that the algorithm can significantly improve attitude estimation accuracy during different satellite operating modes. At last, the filter robustness is assessed by simulating the system with huge errors. This test shows that the filter can converge even in such a challenging scenario, providing excellent accuracy. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：太空技术的进步允许小型卫星来完成只有昂贵和昂贵的平台才能完成的特派团。小型传感器的质量和准确性也有所提高，导致更好地了解航天器态度。然而，这种平台的集成和组装过程具有经常妨碍设备的高精度放置和校准的限制。这转化为传感器测量中的三个最常见的错误：偏差，未对准和非正交性。这项工作提出了一种新的算法，旨在估计和校正基于矢量观察的任何传感器的这三个错误源。该算法基于具有非惯性信息的惯性导航系统使用的相同原理。传播者基于与其他传感器提供的初始估计的陀螺读数计算姿态。同时，卡尔曼滤波器估计姿态和传感器错误。过滤收敛后，估计用于纠正姿态知识。执行可观察性分析，显示过滤器可以正确估计错误状态的条件。之后，测试所提出的技术，在经过验证的卫星模拟器中采用蒙特卡罗模拟。结果表明，该算法在不同卫星操作模式下可以显着提高姿态估计精度。最后，通过模拟具有巨大错误的系统来评估滤波器鲁棒性。该测试表明过滤器即使在这种具有挑战性的情况下也可以收敛，提供优异的精度。 （c）2021 Elsevier Masson SAS。版权所有。

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