# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods
#### 4.2.2 NN as the INS Errors Predictor for Error Compensation  
There are numerous methods of enhancing the INS/GPS integrated navigation system when GPS is unavailable. The concept of mistake forecasting and compensating techniques is illustrated in Figure 12. In order to boost accuracy in the event of a GNSS signal blockage or INS failure, the NN accepts the INS position and time as inputs and outputs the position error. The biggest problem with INS is its \"drift error,\" which becomes worse as more travel is done. Because the IMU is a sensing free against external interference, it is feasible to develop a knowledge base regarding the relationship between motion sequences and sensor efficiency through metrics. If the NN is properly trained, the output keeps the EKF operating as if the GNSS were available for INS error correction. Most of the time, GNSS is accessible and can provide precise input for the ML algorithms during training and optimization. After training, the NN can be utilized to enhance the performance of the INS in urban settings and situations without GNSS. A conventional EKF is utilized in hybrid fusion techniques in addition to NNs that simulate INS or GNSS faults. To achieve high efficiency or to lessen the complexity of designs, these strategies mix estimate and intelligent procedures. The NN utilized in this method accepts the INS position and time as inputs and outputs the position error. The neural network, which was well trained while the GPS signal was present, is used to improve the INS measurements when the GPS signal is lost. In order to reduce the dependency on the INS errors, an input delay neural network (IDNN) has been developed to simulate INS position errors based on current and historical samples of INS positioning, and it performed better during GPS failures. The fundamental concept behind these neural network-based techniques for low-cost GPS and INS is to gather when GPS is accessible; INS data and KF results are used as a training dataset to train the neural network model. The outputs of the KF are then projected by a neural network to compensate for the INS inaccuracies during GPS outages. A predictive approach is based on a linear basis function NN and an updated UKF improves the accuracy of position and velocity of the GPS/SINS-integrated navigation system, especially in the case of GPS signal outages. Additionally, to boost the dependability of the navigation system, a radial basis function NN and a nonstationary time series analysis are applied to forecast and compensate for the positional inaccuracy of the GPS/SINS incorporation in the presence of GPS signal outages. To address the low precision of vehicle inertial navigation in covert environments, ANFIS-based inertial navigation error estimation was developed. The findings imply that the ANFIS can significantly increase inertial navigation's positioning accuracy. The error prediction by ANFIS was frequently greater at this point than the INS solution error of the multi-sequence experiment due to  
Figure 12: The training and prediction phases for error prediction and compensation. the modest INS solution error, and the effect was worse on the road segment with more bends. Future research should enlarge the training set of winding roads and modify the initial input values of the ANFIS model to accommodate this. When a signal is available, GPS displays its recommended outputs. The neural network then acquires accomplished navigational information by changing the synaptic weights. The learning algorithms lower the INS estimation error to achieve the best values for the system parameters as long as GPS signals are available. In contrast to a conventional approach, a model predictive filter (MPF) is created by optimizing the network weights based on the neural network's error correction utilizing system state variables.