Improving the Navigation System of a UAV Using Multi-Sensor Data Fusion Based on Fuzzy C-Means Clustering

Abstract

In this paper a multi-sensor data fusion technique is applied to aerosonde unmanned aerial vehicle (UAV) model to enhance inertial navigation system. An inertial measuring unit (IMU) error model is built with different error parameters (biasing, scale factor, and noise). Each IMU output is applied to strap down inertial navigation system (INS) algorithm to obtain the navigation information: position, velocity, and attitude (PVA). Multi-sensor data fusion algorithm based on fuzzy c-means clustering (FCM) is used to fuse the IMUs data. The fused output is applied to the INS algorithm to obtain the PVA. The simulation results show the effectiveness of the proposed method in reducing the error in navigation information PVA than using a single IMU.