A wavelet de-noise model and wavelet neural network model are presented to predict drift errors of FOG and to enhance identification precision. The method utilizes wavelet analysis to remove high frequency noise and to improve its signal-to-noise ratio. De-noise signal is looked on as the desired output. Then

the network is trained and the weight matrices of neural network are modified by the recursive least square method with forgetting factor. This algorithm without any matrix operation can reduce the computation time and greatly improve real-time performance at fast convergence speed and high precision. The simulation results show that the identification error is within 1.5%. The method establishes the foundations for identification and compensation of the drift error effectively and accurately in inertial navigation systems.