GPS/INS组合导航非线性系统最优估计算法中,基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声.本文针对观测异常会影响残差向量的数字特性分布,提出了一种神经网络辅助的多重渐消因子自适应SVD-UKF算法.该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响,利用奇异值分解抑制UKF中先验协方差矩阵负定性变化,同时构造多重渐消因子对预测状态协方差阵进行调整,使得不同的滤波通道具有不同的调节能力,高效地应用于多变量复杂系统.最后利用车载实测数据进行了验证.结果表明,神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响,拓展了多重渐消因子的应用范围,使其能在观测值含有粗差的条件下自适应调节不同滤波通道,消除滤波状态中的异常,提高组合导航解的精度和可靠性.
The predicted residual vectors should be zero-mean Gaussian white noise, which is the precondition for multiple fading factors adaptive filtering algorithm based on statistical information in GPS/INS integration system. However the abnormalities in observations will affect the distribution of the residual vectors. In this paper, a neural network aided adaptive unscented Kalman filter (UKF) algorithm with multiple fading factors based on singular value decomposition(SVD) is proposed. The algorithm uses the neural network algorithm to weaken the influence of the observed abnormalities on the residual vectors. Singular value decomposition instead of unscented transformation is adopted to suppress negative definite variation in priori covariance matrix of UKF. Since single fading factor in poor tracking of multiple variables has the limitation, multiple fading factors to adjust the predicted-state covariance matrix are constructed with better robustness so that each filter channel has different adjustability. Finally, vehicle measurement data are collected to validate the proposed algorithm. It shows that the neural network algorithm can prevent the observed abnormalities from affecting the distribution of the residual vectors, expanding the applied range of the adaptive algorithm. The neural network algorithm aided SVD-UKF algorithm with multiple fading factors is able to remove influences of state anomalies on condition of the observed abnormalities. The accuracy and reliability of the navigation solution can be improved by this algorithm.
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