基于最优估计神经网络的惯导系统初始对准研究

本文研究了一种基于卡尔曼滤波原理权值更新的多层神经网络学习算法，对此算法进行了详细的推证，并将该算法运用到惯导系统的初始对准过程。仿真结果表明了这种神经网络结构用于惯导系统初始对准问题的有效性，既可获得与卡尔曼滤波器相同的对准精度，又提高了系统的实时性。从而得到了利用神经网络解决惯导系统初始对准问题的一种有效算法。

Initial alignment of the inertial navigation system based on a neural network suitable for the best estimation

As a rule,the Kalman filter has been used to solve the initial alignment of inertial navigation.Whereas the computer time of the Kalman filter depends on the dimension of the inertial navigation system model state vector.The number of computations per iteration is on the order of.Any more number of states would take leave of real time in computation time.We all know that the neural network has the ability of self learning and good performance of real time.A learning algorithm for multiplayer neural network based on the Kalman filter theory has been studied.The theoretical procedure of the algorithm is described in detail.Then,it is used to the initial alignment of the inertial system.Simulation results prove the availability of the neural network algorithm for initial alignment of the inertial navigation system.Not only can surely alignment accuracy be obtained,which is similar to that of the Kalman filter,but also the alignment time is reduced considerably.Consequently,an available algorithm of the neural network for the initial alignment of the inertial navigation system is discovered.