抗野值自适应卫星/微惯性组合导航方法

Adaptively outlier-restrained GNSS/MEMS-INS integrated navigation method

针对在复杂城市环境下卫星导航系统(GNSS)定位定速存在野值,导致GNSS/微惯性(MEMS-INS)组合导航状态参数滤波估计精度恶化,甚至滤波发散的问题,提出了一种抗野值自适应GNSS/MEMS-INS组合导航算法,以提高组合导航精度和可靠性。该算法利用Allan方差分析建立较为精确的MEMS器件噪声模型,有效降低模型异常和状态扰动的影响。同时利用新息序列构造观测异常检验统计量,并根据该统计量构造自适应新息加权因子调节滤波增益矩阵,削弱观测野值对状态估计的不良影响。实验结果表明,该算法能够有效地控制GNSS定位定速异常的影响,具有较强的实时性和容错性。相比于传统算法,车载定位、定速和定姿精度分别提升35.78%、60.19%和82.41%,验证了本文算法的有效性和实用性。

Aiming at the problem that inevitable outliers occur in GNSS position and velocity for land vehicular navigation under complex urban environment, which would deteriorate the estimation accuracy of GNSS/MEMS-INS navigation state parameters and even lead to the filtering divergence, in this paper an adaptively outlier-restrained GNSS/MEMS-INS integrated navigation algorithm is proposed to improve the accuracy and reliability of integrated navigation based on the fault-tolerant adaptive Kalman fihering. This algorithm establishes accurate noise model for MEMS-based inertial sensors with Allan variance analysis technique, which reduces the influence of kinematic model mismatch and state disturbances effectively. The innovation sequences are used to construct the test statistic for detecting observation outliers. The adaptive innovation weighting factor is constructed according to the statistic to adjust the filter gain matrix, and weaken the adverse influence of observation outliers on state estimation. The field test result indicates that the proposed algorithm can effectively control the influences of GNSS position and velocity outliers, and has strong real-time and fault-tolerant ability for GNSS/MEMS-INS integration navigation. The estimation accuracies of position, velocity and attitude determination are improved by 35.78% , 60. 19% and 82.41% , respectively compared with those of traditional algorithm, which verifies the effectiveness and practicability of the proposed algorithm.