【摘要】 在半潜式起重平台立柱快速压载舱的液位测量过程中，由于可能存在传感器故障和外界干扰等因素，从而产生观察野值并导致系统输出的可靠性降低。针对此问题，本文提出一种基于多传感器鲁棒容积卡尔曼融合（Multi-Sensor Robust Cubature Kalman Fusion,MSRCKF）的液位估计算法。借鉴Huber等价权函数的思想，引入一个自适应因子以抑制观测野值对容积卡尔曼滤波的影响，再通过基于扩展信息滤波的矩阵变换推导出近似等效于集中式融合的MSRCKF算法。仿真结果表明，MSRCKF算法可以在单传感器发生故障的情况下有效地提高液位测量系统估计值的可靠性和稳定性。更多还原

【Abstract】 In the level measurement of the column side ballast tank of a semi-submersible crane vessel, the observation outliers may occur and the reliability of the system output is reduced due to sensor failure or external interference. To solve this problem, a level estimation algorithm based on multi-sensor robust cubature Kalman fusion(MSRCKF) was proposed in this paper. Inspired by the idea of the Huber equivalent weight function, an adaptive factor was introduced to suppress the influence of observational outliers on the cubature Kalman filter, and the MSRCKF algorithm was derived through matrix transformation based on extended information filtering, which is approximately equivalent to the centralized fusion. The simulation results show that the MSRCKF algorithm can effectively improve the reliability and stability of the level estimation system in case of sensor failure.更多还原