# 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
In addition, the GPS/INS inertial navigation system's positioning accuracy is increased simultaneously when the GPS signal is obstructed and when it is enabled via the EKF and genetic algorithm (GA) neural network approach. In the absence of a genetic algorithm, the neural network optimizes the estimated EKF results. When the GPS signal is blocked, this successfully increases the positioning accuracy of the GPS/INS integrated navigation system. The integrated navigation system's velocity accuracy still can be improved by using this technology. After the GPS signal was retrieved, the vehicle's exact location was predicted using the interacting support vector machine for regression approach, which was trained using the GPS and INS data. Using the updated online support vector machine for a regression model to correct the positioning inaccuracy of the INS, the longitude and latitude coordinates of a vehicle were ascertained in the absence of a GPS signal. Furthermore, inertial sensor and GPS data were fused in real time using neuro-fuzzy modules. Through the use of cross-validation by using a temporal frame throughout the update process, this research enhanced the performance of these modules. Uncompensated INS readings and DGPS measurements are combined using the INS/GPS integration approach based on ANNs. The commonly used sliding window technique is not taken into account by the ANFIS-based system. Therefore, the size of the temporal window used has a significant impact on the system's accuracy. The findings demonstrated that the suggested system is a dependable, modeless, platform-independent module that does not require any a priori knowledge of the navigational tools being used as a cutting-edge SINS/GPS integration approach that makes use of the Hopfield NN and produces the best state estimation by reducing the Hopfield neural network's energy function. Artificial neural networks could be used to create the \"intelligent navigator,\" a different INS/GPS  
Figure 13: The (**a**) training and (**b**) prediction phases of RBFNN for measurement reconfiguration based on NN. integration strategy, for the upcoming generation of land vehicle navigation and positioning applications. Results from actual land vehicle tests showed that it is possible to use stored navigation knowledge to give accurate, real-time positioning data for standalone INS-based navigation. For comparatively brief periods of time without GPS, the KF outperformed intelligent navigation. The findings of this study significantly support the idea that for INS/GPS-integrated land vehicle navigation processes, the dynamic navigator might be the main mechanism. The ANFIS-based module's goal was to combine INS and GPS position data in real time. However, some restrictions on how the ANFIS parameters could be optimized while the system was in operation created a significant computational burden for real-time implementation. Fuzzy control is crucial because it improves the performance of a coupled GPS/INS for air and land navigation application during GPS signal loss. The use of a linguistic rule to represent complex, nonlinear, time-changing phenomena is a very beneficial and effective strategy. In their approach, a GPS/INS linked navigation system lacking GPS signals is intelligently assisted. The Z-12 GPS receiver and a navigation-grade INS were utilized to provide precise data for ANN, which is employed for multi-sensor systems and only works on location. The GPS/INS integration is based on artificial intelligence, and the AI-based INS/GPS integration methods have used an ANFIS by adopting a temporal-window-based cross-validation methodology during the training process. The suggested fuzzy system is trained using the same data to produce the corrected navigation data. The system will be able to complete its mission without GPS data using only the INS data and fuzzy correction algorithm. This work demonstrates the precision of trained ANFIS in the inability to retrieve data from a benchmark navigation system.