基于自适应SSUKF的组合导航信息融合方法

针对车载组合导航系统噪声统计特性无法事先实时获取的问题,提出了一种神经网络辅助的自适应SSUKF信息融合算法.该算法利用神经网络在线估计系统噪声,采用SSUKF同时估计系统状态和在线训练神经网络的权值,从而能在系统噪声统计特性未知的情况下获得组合导航系统的实时最优估计,给出了算法的详细实现过程.最后,针对车载INS/GPS组合导航系统的信息融合问题进行了仿真研究.仿真结果表明,该算法在系统噪声统计特性未知的情况下仍能获得高精度的估计效果,同时与自适应UKF算法相比,有效降低了算法的计算量,提高了算法运行的实时性,证明了该算法是一种有效而实用的方法.

Information fusion method based on adaptive SSUKF for integrated navigation system

The unscented Kalman filter(UKF) is studied as a state estimation method for the nonlinear system and is used to train multilayered neural networks by augmenting the state with unknown connecting weights.Because the computational costs of UKF are proportional to the sigma points,the UKF can not meet the real time requirement in system state estimate.The neural network-aided adaptive spherical simplex unscented Kalman filter(SSUKF) for the vehicle INS/GPS integrated navigation system is studied.In this algorithm a multiplayer neural network is used to estimate the system noise.And the SSUKF is used to estimate the state vector of vehicle INS/GPS integrated navigation systems and online train the multilayer neural network.The theoretical procedure of this algorithm is described in detail.Then,this algorithm is used in integrated navigation system when the statistic of system noise is unknown.Simulation results prove the availability of this algorithm.Not only can surely estimate accuracy be obtained,which is similar to that of the neural network-aided adaptive UKF,but also the run time is reduced considerably.