Abstract:

The inherent random errors of gyroscopes accumulate more and more over time. Recurrent neural networks are widely used as an effective algorithm for processing time series signals. However, the traditional recurrent neural network (RNN) can not solve the longterm dependence in dealing with the random errors generated by the gyroscope, and it is prone to the problems of gradient disappearance and gradient explosion. In order to obtain accurate gyroscope signals, a denoising algorithm for gyroscope signals is proposed based on a variant of Long short term memory (LSTM) and gated recurrent unit (GRU).And the two networks are innovatively combined to verify. The random error of the original gyroscope is firstly analyzed through Allan variance, and then the output signal of the gyroscope is compensated based on the combination of LSTM and GRU. The results show that LSTM combined with GRU can significantly improve the random error processing of the gyroscope. X, Y, Zaxis gyroscope’s quantization noise, angle random walk, zerobias instability, angular velocity walk and speed ramp performance have been improved to varying degrees.

Key words: gyroscope')">

gyroscope, allan variance, recurrent neural network, gated recurrent unit