A Novel Prediction Algorithm of DR Position Error Based on Bayesian Regularization Back-propagation Neural Network

Abstract

It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS) during GPS outages, a novel prediction algorithm of dead reckon (DR) position error is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. DR, GPS position data are first de-noised and compared at different stationary wavelet transformation (SWT) decomposition level, and DR position error data are acquired after the SWT coefficients differences are reconstructed. A neural network to mimic position error property is trained with back-propagation algorithm, and the algorithm is improved for improving its generalization by Bayesian regularization theory. During GPS outages, the established prediction algorithm predictes DR position errors, and provides precise position for VNS through DR position error data updating DR position data. The simulation results show the positioning precision of the BRBP algorithm is best among the presented prediction algorithms such as simple DR and adaptive linear network, and a precise mathematical model of navigation sensors isn’t established. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2852