# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods
#### 4.2.3 Measurement Reconfiguration Based on NN
Without knowing how many neurons are contained in their buried layers, RBF networks can be used. Instead, throughout the training process, hidden neurons are formed dynamically to achieve the desired performance. When the two systems are cooperating, the measured raw data are typically used to train the RBF neural network in an RBFNN-assisted integrated navigation filtering process. When the GPS malfunctions, the inertial navigation system's observed and calculated values are sent into the expected error value is calculated using a trained network model, and the inertial navigation system subsequently makes adjustments to generate the final navigational data. Superior estimation accuracy is provided by the integrated navigation system backed by the RBF neural network when short-term GPS data are absent. These methods can significantly increase positioning precision despite using different prediction models than the pure INS mode. The loosely coupled GNSS/INS integration model is the main focus of the aforementioned research. A fusion technique based on RBFNN for bridging GPS failures was suggested for the closely coupled model. This algorithm used historical measurements to predict readings for the current window and then continued to predict measurements for the following window. In times of GPS failure, this technique can increase filtering accuracy. The RBFNN is much easier to create than other networks and has a higher learning speed and accuracy. To acquire the pseudo-GNSS measurement when the flawed measurement is identified, this paper employs the RBFNN. Better fault isolation and system reconfiguration are the goals of this. The outcomes of the GPS/SINS KF and GPS KF are combined using the adaptive track fusion algorithm in the tightly coupled GPS/SINS-integrated navigation system based on adaptive multi-sensor track fusion. The lack of readings from the two KFs will cause the system's accuracy to rapidly decline in the event of a GPS failure. To increase the accuracy of the navigation system during GPS failures, a radial-basis-function-neural-network-based solution for bridging GPS outages is given. This approach makes use of a radial basis function neural network to forecast the GPS/INS KF measurement during GPS outages, ensuring the filter's consistent operation and dependable system performance. This RBF-based module's main flaw was that it continued to train using all INS and GPS data that were accessible before the GPS outage, which we found to be unworkable and nearly impossible to apply in real time due to the lengthy training period. With the help of the adaptive track fusion algorithm, this research attempts to improve the precision of a tightly connected GPS/SINS integrated navigation system in the event of a GPS failure. The RBF neural network predicts observations of the differential pseudorange, differential pseudorange rate, and GPS/SINS KF so that the GPS/SINS KF can continue to function normally during GPS failures. The reliability and accuracy of the tightly coupled GNSS/INS are ensured by appropriate fault isolation and a system reconfiguration methodology. However, a thorough comparison and analysis of their performances in the literature must be performed. This study compares their theoretical performances under various scenarios before analyzingtheir underlying concepts. The pseudo-GNSS measurement for the measurement reconfiguration is predicted using the RBFNN in Figure 13. Additionally, to make the switch from system testing to measuring reconstruction feasible, an adjustable modification criterion is offered while taking into account the variety of observation conditions. In the event of a GPS signal interruption, the error compensation model integrating the UKF and BPNN methods might offer continuous, accurate, and dependable vehicle navigation. The BPNN was updated while the GPS signal was active. The well-defined BPNN produced navigation estimation errors when the GPS signal was not available.