Seamless Integrated Orientation Method and System for Bioinspired Polarization Compass/Inertial Navigation System

The accuracy of the bioinspired polarization compass (PC) is easily affected by complex environmental conditions such as cloudy weather, tunnels, building occlusion, and so on. It is difficult to provide continuous orientation information, while the error of inertial navigation system (INS) is easy to diverge over time. As a result, the two orientation means above mentioned can be integrated through information fusion methods such as Kalman filter to improve the performance of the entire integrated orientation system with its good autonomy and complementarity with each other. This chapter focuses on the research of a bioinspired polarized light compass/inertial navigation system (PC/INS) seamless integrated orientation method and system based on self-learning multi-frequency residual correction. First, a integrated orientation model for PC and INS is constructed using a multi-frequency volumetric Kalman filter (CKF-MR) fusion algorithm. Secondly, a Cubature Kalman filter (CKF-MRC) fusion algorithm based on multi-frequency residual correction is proposed to address the issues of low data output frequency and overall system orientation accuracy in the PC/INS integrated orientation system when PC information is not affected. Finally, when the PC is temporarily unavailable due to occlusion or other factors, a self-learning seamless integrated orientation method based on long short-term memory (LSTM) neural network is proposed for the PC/INS integrated orientation system. The seamless integrated orientation method based on CKF-MRC proposed in this book has been validated through UAV test. This integrated orientation method is able to effectively improve the data output frequency and orientation accuracy for the PC/INS system when the PC is not affected. When the PC is temporarily unavailable due to occlusion or other factors, it can still maintain high orientation accuracy and ultimately improve the robustness of the entire integrated orientation system.