Angular Velocity Prediction of GFSINS Based on BP Neural Network

Abstract

Aiming at low precision for traditional angular velocity algorithms in GFSINS, a BP neural network algorithm without complex mathematic computation is put forward to calculate angular velocity. Based on a ten-accelerometer configuration scheme, the accelerometer output, sample interval and fixed position are chosen as input, angular velocity got by lognormal algorithm is chosen as output, and 5000 sample data is trained in the several conditions with different hiding layers, neural cells and training steps. Then a three-layer BP network model with 30 hiding layer neural cells is built. Finally, the angular velocity is predicted in real time by the model. Results show that network has strong adaptive capability and real time, and compared with lognormal algorithm, prediction time is almost equal, but prediction precision of angular velocity is nearly improved by three times.