摘要
设计了基于四元数的捷联惯导非线性初始对准模型,同时指出该模型仅仅是姿态误差四元数和速度误差的非线性函数,而对于惯性器件误差而言则是线性的。针对该模型的部分线性特性,设计了基于边缘采样的UKF滤波算法,该算法仅对状态量中的非线性子集进行采样,因此对于部分线性模型而言,该算法在不损失滤波精度的前提下能够有效降低算法计算量。仿真及车载实测数据实验表明所研究的初始对准模型和相应的滤波算法是有效的,而且较传统方法具有明显的计算量方面的优势;在达到相同对准精度的前提下,所设计算法的计算量较传统算法降低了52%。
A quaternion-based nonlinear model is designed for the initial alignment of a strapdown inertial navigation system(SINS) with large initial errors. It is pointed out that the model has only the nonlinear function relation with quaternion-based misalignment error and the velocities error, whereas it has linear relation with the errors of inertial sensors. In view of this partially linear characteristic, a unscented Kalman filter(UKF) algorithm with marginalized-sampling-based unscented transformation(UT) is designed, which only samples the nonlinear subset of state variables, and can effectively reduce the computation burden without losing filtering accuracy. Simulations and car-mounted experiments demonstrate that the marginalized UT-based Kalman filter can achieve at least a comparable performance to the traditional UT-based Kalman filter with a significantly lower expense. Compared with the traditional algorithm, the investigated method can reach the same alignment precision but with 52% reduced computational burden.
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2014年第5期612-618,共7页
Journal of Chinese Inertial Technology
基金
国家自然科学基金(61304241
61374206)
关键词
捷联惯导
初始对准
UKF
边缘采样
SINS
initial alignment
unscented Kalman filter
marginalized sampling
