一种弹道修正弹SINS任意滚转角快速粗对准方法

A Rapid Coarse Alignment Method for SINS of Trajectory Correction Projectile at Random Roll Angle

针对弹道修正弹药出炮口后滚转角处于随机状态,捷联惯导系统(SINS)失准角过大时卡尔曼滤波收敛困难的问题,提出在卫星拒止环境下利用神经网络快速估计初始滚转角的改进方法。在炮口处布设少量导航信标,建立反向传播(BP)神经网络拟合初始滚转角与观测量间的非线性映射模型。针对信标辅助下姿态弱可观的问题,引入惯导测量参数作为输入,提高网络估计精度。采用主成分分析法进行特征提取,简化网络结构。仿真结果表明,与基于非线性卡尔曼滤波的对准方法相比,本算法可实现任意滚转角下的快速粗对准;对射角、初始俯仰角误差未在训练范围内以及存在布设误差等场景也进行了测试,与未优化的BP网络相比,对准精度更高,鲁棒性更好。

Aiming at the problem that the roll angle of a trajectory correction projectile is in a random state after launch,and the Kalman filter is difficult to converge when the misalignment angle of the strapdown inertial navigation system(SINS)is too large,an improved method for rapid estimation of the initial roll angle in the GPS-denied environment is proposed based on the neural network.A small number of radio beacons are set nearby the gun muzzle,and a backpropagation(BP)neural network is established to fit the nonlinear mapping model between the initial roll angle and the observations.Regarding the problem of weak attitude observability assisted by beacon,strapdown inertial navigation measurement parameters are introduced as input neurons to improve the estimation accuracy.The principal component analysis method is used for feature extraction to simplify the network structure.The simulation results show that,compared with the alignment method based on nonlinear Kalman filter,the proposed algorithm can achieve rapid coarse alignment at any roll angle.Simulation is also carried out on the scenarios where the firing angle error and initial pitch angle error are not in the training range and there are layout errors.The accuracy is higher and the robustness is better compared with the unoptimized BP network.