无人船在恶劣海况下对其船载低成本微机械惯性测量单元（micro inertial measurement unit, MIMU）进行初始对准，其精度不仅会受到海浪摇摆、浪涌等未知复杂干扰的影响，还会受传感器噪声的影响。针对这一问题，提出基于变分贝叶斯的H_∞滤波算法（variational Bayesian cubature H_∞ filter, VBCH），从而实现在系统噪声或状态方程不确定且系统噪声和量测噪声同时具有“时变”“厚尾”特性情况下的自适应鲁棒滤波。仿真实验表明，随着MIMU噪声的增大以及各项误差系数的不稳定性增强，系统噪声和状态方程逐渐变得不确定，基于卡尔曼滤波的自适应算法逐渐失效，而本文提出基于H_∞控制理论VBCH算法则能够继续保持一定的对准精度和算法鲁棒性：对无人船载低精度MIMU的天向对准精度达到1.5°，东向、北向对准精度能达到0.1°。满足无人船大失准角初始对准的要求，具有一定的应用价值。

In order to solve the problem that the initial alignment of low-cost micro inertial measurement unit (MIMU) on unmanned ships under severe sea conditions, which is not only affected by complex disturbances but also the random noise and bias instability of MIMUs, the variational Bayesian cubature H_∞ filter algorithm (VBCH) was proposed. The simulation results show that while both the system noise and measurement noise are "time-varying" and "thick-tail", the VBCH algorithm is still robust. It is also shown that as MIMU's random noise and bias instability increasing, which makes the system noise and state equations gradually uncertain, the VBCH algorithm can maintain alignment accuracy while the robust adaptive algorithms based on Kalman filter algorithm gradually fail. Besides, for unmanned ships with low accuracy MIMU, the alignment accuracy of upward misalignment angle can reach 1.5°, and the alignment accuracy of the eastward and northward misalignment angles can reach 0.1°, which can meet the requirements for initial alignment of unmanned ships with large misalignment angles.