Abstract:

In order to avoid the system accuracy degradation in the initial alignment due to the GPS outage in SINS/GPS/MCP integrated navigation system,a scheme based on neural network and wavelet is proposed,in which the relevant characteristic components denoised by wavelet are treated as the training samples of neural network.Then,a filtering model based the scheme is established,which consists of the equations of misalignment angle,velocity error and position error in SINS system,as well as the state and measurement equations of Kalman filter with velocity and heading matching.Moreover,in order to verify the proposed scheme,simulations are carried out in the conditions of GPS outage,neural network introduction and wavelet denoising.The results indicate that the introduction of neural network avoids the velocity accuracy degradation,and that the retraining of neural network using wavelet denoising effectively improves all the indexes of SINS/GPS/MCP system.It is thus concluded that,as the adoption of wavelet denoising can improve the approximation of neural network to the actual model,the alignment accuracy is further improved.

Key words: integrated navigation, initial alignment, neural network, wavelet analysis