# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods
#### 4.2.1 Prediction of Adaptive Factors for Gain Scaling or Tuning  
It has also been extensively investigated and used to combine new artificial intelligence techniques with integrated navigation systems. The assumption is that the regression models and the adaptive data modeling are both distorted by minimal Gaussian white noise, which is essential for producing accurate estimation results. The EKF offers the best results when used as a navigation state estimator when the noise statistics for the measurement and system are completely understood. In reality, the noise changes with time, this degrades the estimation performance. A common adaptive method for estimating noise covariance matrices is the covariance matching method. The method aims at bringing the actual filter residuals into line with their covariance. However, if the window size is tiny, this innovation-based adaptive estimation produces very erratic results. A hierarchical neural network has been trained to identify the measurement noise covariance matrix in order to solve the problem. The link weights are then iteratively adjusted using the conjugate gradient method via the back-propagation algorithm.  
These input-output linkages are mapped using an NN coupled with the updated KF measurement. The parameters utilized to correct KF gain are the outputs of the NN, while the inputs represent dynamic vehicle circumstances and fluctuations. These techniques typically use straightforward neural networks that must be trained in real time to fill in the necessary missing data. The innovative procedure that comes from the divergence between GPS and system state estimation provides details on measurement noise covariance \(\mathrm{R_{k}}\) and process noise covariance \(\mathrm{Q_{k}}\). The limited information quality of \(\mathrm{Q_{k}}\) and the link between \(\mathrm{Q_{k}}\) and \(\mathrm{R_{k}}\) are the two major barriers to the AKF of integrated navigation systems. Because \(\mathrm{Q_{k}}\) instability can be effectively reflected in the observation sequence of the inertial measurement unit (IMU) in the SINS/GPS incorporated navigation system, \(\mathrm{Q_{k}}\) can be located by identifying these patterns and looking through the basic information. The research employs artificial intelligence in a novel way to calculate inertial sensor noise.  
During GNSS outages, the effectiveness of the GNSS/INS integrated navigation system would be decreased. The cost and complexity of the navigation system will rise, but this solution can produce good navigation performance. The multi-layer perceptron neural networks [66; 67; 68; 69; 70; 71; 72; 73], radial basis function neural networks [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86], and adaptive neuro-fuzzy inference systems [87] are a few examples of ANN-based techniques. Establishing an internal link between IMU/INS outputs and GPS data is the fundamental goal of ANN-based techniques. Furthermore, speed and/or attitude are utilised as inputs to improve prediction. Additionally, several previous data kinds (such as past 1-step and past 10-step data) are introduced to the inputs. Moreover, the model will become more sophisticated as a result of these additional inputs. As long as a GPS signal is present during the training phase, the system is operational. Given that there are fewer measurements in an INS than there are state parameters and that neural networks excel at approximation and at being nonlinear, adaptive, and fault-tolerant, the adaptive factor can be built using neural  
Figure 11: The block diagram of hybrid system for prediction of adaptive factors for gain scaling.  
networks. The aforementioned techniques do not pre-filter IMU data to remove the noise from the first measurement, which will have an impact on the prediction.