# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 1 Introduction
In this field, scholars have proposed various adaptive techniques, including but not limited to Adaptive Kalman Filtering [11; 12] (AKF), Adaptive Finite Impulse Response Filters [13], and Adaptive Square Root EKF [14]. The common goal of these methods is to intelligently adjust the parameters or structural elements of the system model according to changes in system performance and different operating conditions. However, traditional analytical methods often struggle to extract valuable features when dealing with highly nonlinear or noisy data, as they may overlook certain key information during the estimation process. The core challenge in system modeling lies in how to simplify its structure as much as possible while ensuring model accuracy. In practical applications, identifying noise model parameters is particularly challenging, primarily because the noise generated by actual systems is often highly complex. These noises do not follow a single distribution pattern but rather consist of mixed distributions formed by multiple distributions [15; 16]. Therefore, it is difficult to accurately describe this highly nonlinear relationship solely from a mechanistic or data feature perspective. However, with the significant improvement in computer computational power, machine learning methods have regained widespread attention from researchers. Artificial Neural Networks (ANNs) have demonstrated outstanding fitting capabilities [17] when dealing with highly nonlinear problems. Particularly, derivative models based on Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) networks [18] and Gated Recurrent Units (GRUs) [19], have gained favor among many scholars for handling highly nonlinear problems. ANNs offer robust solutions for system modeling and noise parameter identification within the Kalman filtering framework. Currently, researchers are delving into the integration of Kalman filtering and neural networks, focusing primarily on two subfields: external interaction fusion of Kalman filtering and neural networks and internal deep fusion of Kalman filtering and neural networks. Research in these two subfields holds promise for providing new insights and methods for handling complex systems and noise issues. By combining neural networks with Kalman filtering, it is possible to fully leverage the advantages of neural networks in handling complex nonlinear problems and extracting features, as well as the benefits of Kalman filtering in state estimation and noise suppression. Particularly, the combination of Convolutional Neural Networks (CNNs) and LSTM networks demonstrates significant advantages in sequence data processing and feature extraction. CNNs automatically learn and extract spatial features from input data, while LSTMs excel in handling data with temporal dependencies, capturing long-term dependencies, thereby enhancing the accuracy and stability of UAV navigation. Therefore, this paper proposes a Kalman filtering UAV fusion navigation algorithm assisted by CNN and LSTM. The algorithm extracts spatial features from multiple sensor data using CNNs, processes temporal data using LSTMs, and integrates the extracted feature information into the Kalman filtering framework. This algorithm effectively utilizes the complementary nature of multisensor data to improve the accuracy and robustness of UAV navigation systems. This paper first introduces the basic principles of Kalman filtering and its current applications in UAV navigation. Then, it elaborates on the design and implementation process of the Kalman filtering UAV fusion navigation algorithm assisted by a CNN and LSTM, including data preprocessing, feature extraction, and the construction of the neural network framework. Finally, the effectiveness and performance of the algorithm are validated through experiments, and the experimental results are analyzed and discussed.