Shaukat Nabil, Ali Ahmed, Javed Iqbal Muhammad, Moinuddin Muhammad, Otero Pablo
Oceanic Engineering Research Institute, University of Malaga, 29010 Malaga, Spain.
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel). 2021 Feb 6;21(4):1149. doi: 10.3390/s21041149.
The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.
卡尔曼滤波器的变体——扩展卡尔曼滤波器（EKF）和误差状态卡尔曼滤波器（ESKF），在水下多传感器融合的定位与导航应用中被广泛使用。由于这些滤波器是通过在误差协方差矩阵中采用一阶泰勒级数近似来设计的，在高非线性情况下会导致估计精度下降。为了解决这个问题，我们提出了一种用于水下航行器定位的新型多传感器融合算法，该算法通过用径向基函数（RBF）神经网络增强ESKF来提高状态估计。在所提出的算法中，RBF神经网络用于通过改进新息误差项来弥补ESKF性能的不足。RBF神经网络的权重和中心通过使用最速下降优化方法最小化估计均方误差（MSE）来设计。为了测试性能，使用蒙特卡洛模拟在三种不同的实际场景下，将所提出的RBF增强型ESKF多传感器融合与传统ESKF进行了比较。我们发现，尽管存在高非线性度、建模不确定性和外部干扰，我们提出的方法仍能提供更好的导航和定位结果。
