# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Neural Network Structure and Function  
With the advancement of Artificial Intelligence (AI) and deep learning, the application of neural networks in fusion navigation primarily focuses on the integration and processing of multisource navigation information. By constructing appropriate neural network models, data from different navigation systems can be effectively integrated to enhance navigation accuracy and stability.  
Specifically, there are various ways in which neural networks are applied in fusion navigation [27]. One common approach is to leverage the self-learning and feature extraction capabilities of neural networks to process data from different navigation sensors, extract useful feature information, and fuse them. This enables the full utilization of the advantages of each sensor, compensating for the limitations of individual sensors, and improving the overall performance of the navigation system. Another approach is to utilize the predictive capabilities of neural networks to forecast the future state of the navigation system. By learning and analyzing historical data, neural networks can capture the motion patterns and trends of the navigation system, thereby accurately predicting future states. This facilitates early detection of navigation errors and corrections, thereby enhancing navigation accuracy and reliability. Additionally, neural networks can be combined with other algorithms and technologies to form more robust fusion navigation solutions. For example, neural networks can be combined with Kalman filter algorithms to filter navigation data, further reducing the impact of noise and errors. Alternatively, neural networks can be combined with map-matching techniques to constrain and optimize navigation results using map information.  
The neural network is a mathematical model algorithm that mimics the behavior characteristics of animal neural networks. It achieves information processing by adjusting the relationships between a large number of interconnected nodes internally. It possesses adaptive self-learning capabilities, automatically grasps environmental features, achieves automatic target recognition, and exhibits advantages such as good fault tolerance and strong anti-interference ability.  
Structurally, neural networks are composed of a large number of neurons interconnected with each other. These neurons receive input signals, process them through activation functions, and then output signals to the next layer. Different neural network models have different structures, such as perceptrons, feedforward networks, residual networks, and RNNs, among others.  
Specifically, the perceptron [28] is the most basic of all neural networks and serves as the fundamental component of more complex neural networks. It connects only one input neuron and one output neuron. Feedforward networks [29] are collections of perceptrons, consisting of input layers, hidden layers, and output layers, with signals propagating unidirectionally between these layers. Residual networks [30] achieve signal propagation across layers by skipping connections, thereby reducing the problem of gradient vanishing.  
RNNs [31] contain loops and self-repetition, enabling them to handle data with temporal dependencies.