Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation
Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation
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DOI：
10.1109/lsp.2006.871854
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发表时间：
2006-06
影响因子：
3.9
通讯作者：
Ronghui Zhan;Jianwei Wan
中科院分区：
文献类型：
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作者：
Ronghui Zhan;Jianwei Wan
扩展卡尔曼滤波器（EKF）作为非线性系统的状态估计方法是众所周知的，并且已经被用于通过用未知连接权重来增强状态来训练多层神经网络（MNN）。然而，扩展卡尔曼滤波存在线性化不稳定、雅可比矩阵计算量大等固有缺陷，尤其是在非线性严重时，其性能会大大下降。在这封信中，首先，一个更强大的学习算法MNN基于无迹卡尔曼滤波器（UKF）的推导。由于它给出了一个更准确的估计的链接权重，收敛性能得到改善。然后，该算法进一步扩展到开发一个神经网络辅助UKF的非线性状态估计。在该算法中的神经网络是用来近似的不确定性的系统模型，由于错误建模，极端的非线性等UKF是用于神经网络在线训练和状态估计同时进行。仿真结果表明，新算法是非常有效的，与传统方法相比，在非线性滤波中更接近最优方式
The extended Kalman filter (EKF) is well known as a state estimation method for a nonlinear system and has been used to train a multilayered neural network (MNN) by augmenting the state with unknown connecting weights. However, EKF has the inherent drawbacks such as instability due to linearization and costly calculation of Jacobian matrices, and its performance degrades greatly, especially when the nonlinearity is severe. In this letter, first a more robust learning algorithm for an MNN-based on unscented Kalman filter (UKF) is derived. Since it gives a more accurate estimate of the linkweights, the convergence performance is improved. The algorithm is then extended further to develop a NN-aided UKF for nonlinear state estimation. The NN in this algorithm is used to approximate the uncertainty of the system model due to mismodeling, extreme nonlinearities, etc. The UKF is used for both NN online training and state estimation simultaneously. Simulation results show that the new algorithm is very effective and is closer to optimal fashion in nonlinear filtering compared with traditional methods