Generalized Dynamic Fuzzy NN Model Based on Multiple Fading Factors SCKF and its Application in Integrated Navigation

Traditional neural network (NN)'s generalization ability is weak, and its prediction accuracy depends heavily on the selection of network structure and training samples, so it cannot be directedly applied to the strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS) integrated navigation system in varied environment. Aiming at these two problems, based on the fuzzy neural network (FNN) model, a new neuron growth-attenuation mechanism is established by introducing the dynamic adjustment idea of network structure. Therefore, the network has a compact structure and good performance, which prevents the network from over-training and over-fitting. Besides, the theory of strong tracking filter (STF) is introduced into the nonlinear filter to design the multiple fading factor square root cubature kalman filter (MSCKF) method for neural network parameter training, which shortens the training time and improves the convergence speed. Results of the simulation and physical experiment verification demonstrate that the generalization ability of proposed model is enhanced and prediction accuracy is improved during the GNSS signal loss. Compared with the pure inertial navigation method, the position errors in latitude and longitude are reduced by 85.00%, 89.71% and the velocity errors in east and north are reduced by 94.57%, 83.11%, respectively by the proposed generalized dynamic fuzzy NN Model Based on MSCKF(MSCKF-GDFNN).

Automated summary

By introducing a new neuron growth-attenuation mechanism based on the fuzzy neural network model and incorporating the theory of strong tracking filter, a generalized dynamic fuzzy neural network model based on MSCKF (MSCKF-GDFNN) is proposed, which demonstrates improved generalization ability and prediction accuracy during GNSS signal loss.