# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 6 Future Challenges  
This article discusses the use of an ANN in the Global Navigation Satellite System, which includes the Global Positioning System and the Inertial Navigation System. The study also shows that depending on the application scenario, combining the INS with an external navigation system like GNSS may be a state-of-the-art method. Because it provides consistent, accurate, and dependable navigation resolution, the GNSS/INS navigation system is presented. The Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), sequential Monte Carlo (SMC) techniques, and particle filter (PF) are also explored as several filtering approaches. The geometric dilution of precision (GDOP) approximation, GPS navigation processing, attitude determination using NN, and prediction of differential DGPS correction using NN are reviewed. Inaddition to this, the most popular technique for training a multilayer feed-forward artificial neural network, the back-propagation neural network (BPNN), is analyzed. Two forms of NN approximators--BPNN and GRNN--as well as four types of NN classifiers--BPNN, OI Net, PNN, and general regression neural network (GRNN)--are well defended. The two main strategies for AKFs that have been established are innovation-based adaptive estimating (IAE) and multiple-model-based adaptive estimation (MMAE), which is similar to the interacting multiple models (IMM) algorithm. The technique uses variational Bayesian (VB) learning to give a robust tracking capacity for time-varying noise covariance and to approximate noise strength. Due to their ability to address the problem of non-linearity, various techniques, including those based on multi-layer perceptron neural networks (MLPNNs), RBFNNs, random forest regression (RFR), and adaptive neural fuzzy inference systems (ANFIS) were also described for GPS/INS systems. A model will be trained using further datasets in future studies, and more data will be gathered under more challenging conditions. Some are listed below:  
* The flexibility of neural networks to consistently modify their structure to the application can be linked to its significance for GPS/INS incorporation. * However, for selecting an appropriate window size for real-time, practical applications requires considerable effort. Additionally, the efficiency of the approach is highly dependent on the vehicle's state of movement and can be regained by using an ANN. * The CNNs particular models are designed to take two-dimensional input data, including images or time series data. The linear regression procedure known as \"convolution\" is always included in at least one layer of the network and can be the source of the term for these topologies. * The NNs that can be used to build waveforms for segments with curves differ from those that can be used for sequences with major roadways; this information may be obtained by subtracting the road curvature from the steering angle. * A variety of neural networks and other machine learning techniques can be chosen to enhance the proposed model's hidden layer and transfer function counts as well as the number of invisible neurons and parameters. * By adding approaches that can offer a priori knowledge about the level of chaos of the time series, the recommended framework will perform better when choosing between adaptive, optimum, and robust estimators. When these strategies are paired with parallel computing activities, they might be able to train the methodology more effectively for longer prediction horizons. * Displacements can grow to be impossibly large or incredibly small when they are accumulated over a long period. Expanding gradients can be shortened or compressed, making them simpler to solve. On the other hand, collapsing gradients may become too small for computers to express and for networks to learn from them with more efficiency.