# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### More Recent Development of NNs for Navigation System Designs
#### 4.3.2 Long Short-Term Memory (LSTM) NN  
The most popular deep neural networks for supervised learning include convolutional neural networks (CNNs), RNNs, reinforcement learning, and an RNN variant known as long short-term memory (LSTM) models. To create the approximation function, numerous hidden layers are typically stacked together and triggered in a series of steps. The term \"depth\" of a model refers to how many hidden layers are present, which explains how the term \"deep learning\" came to mean learning using models with many layers. When it comes to handling time sequence data and nonlinear system modeling, deep learning algorithms like RNNs offer several advantages over ML approaches because of their unique cycle unit. The inability of RNNs to compute long-term sequences led to the creation of LSTM by researchers. An enhanced RNN that is effective at handling these issues is the LSTM NN. To determine whether the supplied information is useful, LSTM also has the benefit of handling time series sequences with larger intervals and time delays in a cell. The LSTM approach significantly reduced the positioning inaccuracy, although it may have outperformed ANFIS. The IMU core readings have complicated noise properties, as most approaches presume that NNs are constructed under optimal conditions without taking the precision of the labeled data into consideration. Before data fusion, a preprocessing method reduces uncertain noise in the raw values from inertial sensors. This research suggests a preprocessing data method that combines empirical mode decomposition (EMD) and wavelet threshold filtering (WTF) to treat the actual IMU raw measurement to acquire trustworthy and precise location data during GPS failures [106]. A self-learning navigation method that integrates data denoising and LSTM NN in conventional GNSS/INS integrated navigation is analyzed. The INS velocity and yaw as well as the output from the preprocessed IMU are used as the input of the LSTM model when the GNSS signal is available. The outcome is the GNSS position update. In order to correct INS navigation results when the GNSS signal is lost, the pre-processed IMU and INS information are put into the LSTM model to  
Figure 15: The architecture of NARX-NN model. construct a pseudo-GNSS position and transmit it to the EKF. The EMD divides the noisy IMU signal, according to amplitude and frequency, into some intrinsic mode functions (IMFs). Then, the high-frequency IMFs are subjected to wavelet threshold filtering, which differentiates the useful data in the high-speed IMFs. Finally, to create a denoised signal, these IMFs are combined with low frequency IMFs and residual signals. The outcomes of the experiments demonstrate that the suggested navigation algorithm can greatly increase GNSS/INS integrated navigation accuracy and reliability during GNSS failures. A learning model utilizing an RBFNN to map the INS position and location discrepancy between the INS and GPS was developed first. Then, an INS/GPS integration technique comprised of ANN was developed to fuse uncompensated INS and DGPS measurements for a variety of ANNs, i.e., complete counter propagation neural network (Full CPNN), RBFNN, BPNN, forward-only counter propagation neural network (FCPNN), CNN, adaptive resonance theory-counter propagation neural network (ART-CPNN), higher-order neural networks (HONN), and IDNN. Recently, several novel robust algorithms using NNs or support vector machines (SVM) in combination with EKF and ML techniques have demonstrated improved navigation performance. The majority of the systems discussed above rely on static neural networks, which only employ one-step INS data from the past and present without taking into account more historical vehicle dynamic data. It is understood that obtaining precise localization would be made more difficult by the requirement for historical vehicle dynamics via time series data. The aforementioned techniques are unable to maintain precise navigation results when GPS has a prolonged outage.