# 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
Deep learning recently demonstrated satisfactory performance in time sequence prediction, which includes speech and natural language processing. When working with lengthy sequences, RNNs may experience gradient disappearance and explosion. The MEMS-INS accuracy has been substantially improved by the development of LSTM, which was based on RNNs, to determine the INS inaccuracies according to the latest and previous records of the INS outcomes and denoise the MEMS-IMU signals. To calculate the pseudo-GPS, an LSTM focused on convolution NN can be used for readings to assist INS during GPS failures. In addition to the outer recurrence of RNNs, typical LSTM-gated cells in RNNs also feature an inside recurrenceThe internal state that cells store can be read from and written to. Gates regulate the entry, exit, and deletion of data from this cell state. They use their weights, much like a conventional neural network, to block or transmit information based on its significance and strength. Furthermore, RNN and LSTM neural networks were developed. The LSTM networks were created in an effort to address the RNNs' long-term dependency. Creating a gating switch successfully addresses the RNN network's long-term issues [87]. Recent studies have shown that LSTM can be useful in a variety of disciplines, including sustainable navigation. Robotics, ship trajectory prediction, and other industries have made extensive use of LSTM. The MLP network offers superior results and exhibits greater capability than conventional approaches. However, it is unable to manage long-term sequence management and historical dependencies in time-series data. These problems have caused the research community to focus more on CNN and RNN techniques. These techniques, however, were ineffective because of vanishing gradient issues and long-term effects. A fusion technique based on LSTM-NN is proposed to obtain strong prediction performance, maintain the model's simplicity, and adaptively incorporate historical data without increasing the additional inputs, thereby overcoming the limitations of existing ANN methods. The fundamental idea behind LSTM is presented in the following figure in Figure 16. LSTM is an RNN version that addresses RNNs' drawbacks by modeling long-term dependencies using memory cells in place of the hidden layers of RNNs. An activation function and several gates, including input, output, and forget gates, are used by LSTM to represent and learn the behavior of time-based relations. To overcome GPS outages, an intelligent AKF built on deep neural networks, fuzzy logic for integrated navigation systems, and a fusion mechanism to deliver phony GPS location data are proposed. Empirical mode decomposition threshold filtering (EMDTF) and an LSTM NN make up the approach. Inertial sensors' noise is removed by the EMDTF, which also gives more precise data for ensuing calculations. The LSTM predicts the pseudo-GPS position during GPS failures using the current specific forces and angular rates. To remove noise from IMU data, a preprocessing data technique based on EMDTF is described. Finally, the suggested methodology is used to navigate using the INS/GPS combined system [110]. For the unmanned aerial vehicle (UAV), a GPS/INS-integrated navigation system is suggested based on an LSTM NN. However, it can be difficult to manually specify the covariance noise value in KFs, which should dynamically adjust to the motion of the UAV. An LSTM-NN was chosen to create a cutting-edge end-to-end navigation technique because it contains specific contextual memory units that can automatically retain prior useful information. Experimental findings show that the suggested data-driven navigation strategy may accurately estimate the online UAV position without manually creating and modifying a mathematical model. For handling outliers, a reliable adaptive filter based on VB theory is suggested. Its benefit is that it enables time-varying noise adaptation and lessens outlier interference with the integrated navigation system.