Multi-source Navigation Sensor Backend Information Fusion Technology Based on D-S Evidence Theory

Abstract

Multi-source navigation sensors can provide high-precision navigation solutions. To achieve back-end information fusion, this paper presents a fault detection algorithm and an information fusion algorithm. The fault detection algorithm is designed to detect anomalies in multisource navigation sensor data, while the information fusion algorithm is used to integrate data from multiple sensors for obtaining more accurate and reliable navigation information. These two algorithms are based on the D-S evidence theory and Murphy’s improved method, which effectively detect and judge abnormal data and handle conflicts and uncertainties among multiple sources of information, thereby outputting more reliable navigation information. By applying the algorithms to actual flight data, this study demonstrates their effectiveness in abnormal situations, such as abnormal inertial attitude angle, constant difference between inertial pitch angle and heading pitch angle, and sudden changes in GPS output position information.