GPS／SINS超紧组合导航系统自适应混合滤波算法

针对传统反馈校正滤波结构中,由于不可观测状态的反馈导致系统滤波精度下降,以及由于全球定位系统／捷联惯性导航系统(GPS／SINS,global positioning system／strapdown inertial navigation system)超紧组合导航系统量测方程的非线性导致滤波难度的增加等问题,本文重新推导了线性的量测方程,并将基于状态可观测性的混合校正滤波算法应用于该模型．通过对比三种主流可观测性分析方法,选用误差协方差阵的特征值和特征向量可观测性分析方法分析系统状态的可观测性．最后根据可观测性分析的结果制定自适应的反馈因子,从而对SINS和GPS接收机误差进行校正．仿真结果显示,该方法可以有效提高不完全可观测系统的估计精度．

An adaptive revising filtering method for GPS/SINS ultra-tightly coupled system

In conventional feedback filter, the feedback from some unobservable system states will lead to the divergence of navigation errors. Further the measurement equation of global positioning system/strapdown inertial navigation system (GPS/SINS) is nonlinear, and it increases the complexity of the filter. To solve these problems, a linear measurement equation was derived in this paper. As a complement to this measurement model, a composed adaptive revising filtering method based on the observability of states was presented as well. The eigenvalues and eigenvectors analysis method of error covariance matrix was chosen to analyze the observability of system states as a result of the comparison of three popular observability analysis methods. The feedback factors were thus designed according to the analyzed results. As a consequence, errors from the original data of SINS and GPS receiver can be corrected. Simulation results demonstrate that our proposed method is effective and can improve the estimate accuracy of the system in incomplete observable condition.