# 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  
A hybrid fusion technique is accepted to analyze the statistical correlation between sensor data and the GNSS positions advance during GNSS outages. During the GNSS accessibility time, KF merges the INS and GNSS data in order to gain more precise location and velocity information. When it is accessible, the GNSS is used as a reference. A framed knowledge base of the INS's actions in specific vehicle motion outlines is created using the data from GNSS and INS. Multilayer feed-forward NNs with a back-propagation learning method were utilized in the literature to combine INS and DGPS data. Due to the presence of a GNSS and an INS, the combined navigation system offers a constant high-accuracy location. On the other hand, INS error divergence causes the navigation accuracy to unavoidably decrease during a GNSS outage. To predict the measurement of EKF during GNSS failures, the NN is directed when a GNSS signal is present. Throughout training and optimization, GNSS is frequently accessible and can be used as a precise input for ML algorithms. After training, ML can be utilized to enhance the performance of the INS in urban settings and situations without GNSS in addition to the particular force and angular velocity produced by the inertial unit in the input. The model is simple structurally and does not fully utilize the historical data. As a result, the system's response time will be sped up by using an extreme learning machine (ELM) NN to detect rapid GNSS signal failures and cut the detection time. To examine the effectiveness of forecasting the position increment of the GNSS system at the same time, different forms of historical data are input into the upgraded BPNN during GNSS failures. These methods' major goal is to create a map between INS measurements and GNSS measurements, such as angles, forces, speeds, positions, and so forth. The NN is then trained using the SINS and GNSS measurements while the GNSS signals are present. A well-trained NN can provide virtual GNSS measurements after GNSS disruptions occur. INS/GPS integration algorithms based on MLPs and NNs have been proposed and applied to various types and grades of INS, intending to enhance the efficiency of the GPS and INS integrated system during GPS outages. To integrate data from an INS and DGPS using a multilayer perceptron NN, an NN-based satellite has been added to the multisensor system integration technique. Although these methods can increase positioning accuracy, its real-time capabilities are constrained by the complexity of multilayer perceptron networks' design and its online training procedures. To prepare the INS and GPS data for use with the NN, wavelet multi-resolution analysis (WMRA) is used. While the signals are present, EKF estimates the INS measurement errors, as well as the location, velocity, and attitude errors, and offers precise navigational solutions. In addition, the input of the EKF is a multi-layer NN that has been developed to map the driving behavior of the car using INS prediction errors for every GPS era. The NN can be used to anticipate INS mistakes for EKF measurement updates during signal blockages and hence enhance navigational solutions. The INS velocity, IMU outputs, and the length of signal outages are linked to a new model that predicts the GPS position increment. A land vehicle navigation test has been used to empirically assess the performance of the suggested approach. The test results show:  
1. During a GPS failure, the suggested model can effectively estimate the location increment and make up for the accumulation of INS errors. 2. When the GPS measurements are absent, the advantage of a current design on localization becomes increasingly pronounced with time. 3. The BPNN framework can adequately optimize both precision and computational burden by employing inputs for the past and present two-step data. The measurement update of the KF during GPS outages was predicted using a mixed prediction method that includes the RBFNN and time series analysis.