基于非线性Kalman滤波的导航系统误差补偿技术

针对非线性非高斯导航系统信息处理问题,采用自组织算法、神经网络和遗传算法等改进传统非线性Kalman滤波算法,构建一种自适应的组合导航系统。应用具有冗余趋势项的自组织算法、Volterra神经网络和遗传算法,建立导航系统误差的非线性预测模型,进而计算得到其预测值;将该预测值与Kalman滤波算法求得的估计值进行比较得到差值,以此监测Kalman滤波算法的工作状态;采用自适应控制方法,在导航系统结构层面改进Kalman滤波算法,构建新型的导航系统误差补偿模型。开展基于导航系统KIND-34的半实物仿真研究,应用所提出的改进方法改善了导航系统误差的补偿效果,提高了组合导航系统的自适应能力和容错能力。

Technology of error compensation in navigation systems based on nonlinear Kalman filtering

As for nonlinear/non-Gaussian information processing problems in navigation systems, a kind of adaptive integrated navigation system was established based on modified nonlinear Kalman filtering by utilizing self-organization algorithms, neural networks and genetic algorithms. First, applying self-organization algorithms with redundant trends, Volterra neural networks and genetic algorithms, error models of integrated navigation systems are built based on Kalman filtering. Then, predicted values of navigation errors are obtained using the established error models. Second, comparing the predicted values with the estimated values by Kalman filtering, the difference between them, functioning as an index of divergence of Kalman filtering, is formulated. Finally, the modification of nonlinear Kalman filtering is made and a novel technology of navigation error compensation is thus developed based on adaptive control methods. Applying traditional and modified Kalman filtering respectively, the semi-physical simulation study based on the navigation systems KIND-34 and Com-paNav-2 was carried out. The analyzed results indicated that the accuracy of error estimation and compensation in nav-igation systems was improved using modified nonlinear Kalman filtering with self-organization algorithms, neural net-works and genetic algorithm, and thus the ability of self-adaption and fault tolerance was enhanced in integrated navigation systems.