# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Intelligent Estimator Using an NN as a Replacement of KF for Data Fusion  
The fact that it is not necessary to employ numerical methods in the simulation model and measurements with AI-based techniques is a key distinction between them and other forms of estimating methods, such as the Kalman filter. Several ANN-based approaches have been analyzed to take the place of KF as a state estimator. Instead of integrating the KF and the NN, this technique just substitutes one for the other. The main benefit of an NN over a KF is that it does not require any pre-existing statistical or mathematical models to choose a function that best maps inputs to outputs, such as the absolute location shown in Figure 8. Some methods, such as those based on multi-layer perceptron neural networks (MLPNNs) [65; 66; 67; 68; 69; 70; 71; 72], RBFNNs [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84], and adaptive neural fuzzy inference system (ANFIS) [85], were described for GPS/INS systems due to its capability to tackle the issue of nonlinearity. Due to their exceptional ability to model the nonlinear relationship, the aforementioned models were determined to have good performance. However, BP has drawbacks, including poor learning convergence velocity, artificial experience dependence, and a lack of accuracy. The fuzzy inference variables can be written as the NN weight vectors, and the FNN can be implemented as an NN framework. The \"professional previous knowledge\" is simple to transfer into the hazy if-then rules. With learning from the inferred data of individuals, the method can concurrently adopt the inference engine criteria and fine-tune the membership functions. The [PERSON] and Sugeno's fuzzy inference-based ANFIS is a multilayer feed-forward network in which each node carries out a specific node operation on input variables. The ANFIS made of five layers has the architecture as shown in Figure 9.  
An important subset of deep learning that is frequently used for prediction is the recurrent neural network (RNN) [86; 87], which can analyze the time-dependent data processing. Its output is structurally reliant on both the input at hand and the previous output. The hidden layer outputs of the RNN's particular structure are kept in memory, which is treated as another input. The ability to link the current job with past information for the frame is the RNN's most important benefit. In time series prediction, the state-based historical cues are well-predicted using sequence learning and RNNs. RNNs, differently from MLPs, use models in which the output is encoded into a hidden state and is a function of the inputs and outputs from the past. Unlike MLPs, RNNs can encode the information in the sequence itself and have a memory of the past outputs. This type of model cantherefore be useful for learning from sequential data. However, back-propagation over time, a variation of back-propagation that takes temporality into account when computing the gradients, is typically used to train RNNs. When used for lengthy temporal sequences, this approach can cause many problems. The RNN units are made up of hidden state cells that predict events in the future by exploiting temporal signals, with better results than conventional methods. Short-term dependencies are possible with RNNs, and they can store large amounts of data about prior signals. The \"vanishing gradient\" is a training problem that is brought on by long-term dependencies, which the RNNs must be able to model. Figure 10 shows the block diagram for the proposed GNSS/INS integrated navigation with RNN training and prediction modes. Figure 8: Intelligent estimator using an NN as a replacement of KF for data fusion: (**a**) training mode; (**b**) predicting mode. Figure 9: The ANFIS architecture. In neural networks, the nodes in the buried layer are typically not connected. The hidden layer nodes in RNN, however, are connected and have both the input from the input layer and the input from the previously concealed layer as their inputs.