Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

• Published in: Applied sciences 2024-07, Vol.14 (13), p.5493
• Main Authors: Chen, Kai, Zhang, Pengtao, You, Liang, Sun, Jian
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Closed loop systems; convolutional neural network; fusion navigation algorithm; Kalman filter; long short-term memory network; Methods; Navigation systems; Neural networks; Noise; Parameter identification; positioning accuracy; Principles; Sensors; Unmanned aerial vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14135493

In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.