针对不可交换性误差一般采用多子样旋转矢量法对其进行补偿，但传统多子样旋转矢量法在大半锥角或者其他更为复杂的角运动条件下,性能会有所退化，新出现的多子样四元数算法和方向余弦算法没有显式地考虑不可交换性误差，直接求解姿态微分方程。为了综合比较这些算法的性能，在圆锥运动和大角度机动条件下对其进行仿真分析，采用理想和非理想采样两种数据条件，同时变换不同的采样频率，并比较了算法的计算负担。结果表明，在理想条件下，新算法的周期性误差小于旋转矢量法,且子样数的增加使新算法精度明显提高；对比理想与非理想采样两种情况，算法精度相差4~5个数量级。3种算法各有优缺点，需要对惯性器件数据进行充分补偿才能完全发挥算法的精度潜力。

The non-commutativity error is generally compensated by the multi-subsample rotation vector method, but the performance of the traditional multi-subsample rotation vector method is degraded under the condition of large half cone angle or other more complicated angular motion. The new multi-subsample quaternion algorithm and the direction cosine algorithm do not directly consider the non-commutativity error, and the two algorithms directly solve the attitude differential equation. In order to comprehensively compare the performance of these algorithms, a simulation analysis is carried out under conical motion and large angle maneuvering conditions. Two data conditions, ideal and non-ideal samplingare used to transform different sampling frequencies, and comparison is made to the computational burden of the algorithms. The results show that under ideal conditions, the periodic error of the new algorithm is smaller than that of the rotation vector method, and the accuracy of the new algorithm is improved significantly with the increase of the number of subsamples. By comparing ideal sampling with non-ideal sampling, it can be seen that the accuracy of the algorithm differs by 4~5 orders of magnitude. Each of the three algorithms has its own advantages and disadvantages. Only by fully compensating the inertial device data can the accuracy potential of the algorithm be fully realized.