Low Computational Data Fusion Approach Using INS and UWB for UAV Navigation Tasks in GPS-Denied Environments

Abstract

This paper presents a low computational approach for unmanned aerial vehicles (UAV) navigation in GPS-denied environments. This approach is aiming to reduce computation load for UAV flying mission constraints. Small size, light weight on board hardware are constraints for UAV deployment and flying missions. The on board processor should not be built with high complexity and should consume as little computing as possible. Most existing approaches use Kalman filter, extended Kalman filter, Unscented filter, or particle filter to fuse different types of onboard sensor data to estimate UAV position. We developed a data fusion architecture that does not use these filters. We use an ultra-light-coupling fusion architecture. In this architecture, primary sensor and secondary sensor data are fused. When the secondary sensor is unavailable in most of the time, the UAV navigation uses the output of the primary sensor. When the secondary sensor signal is available, the primary sensor is re-aligned using the secondary sensor signal to bond the errors. In our approach, the primary sensor is Inertial Measurement Unit (IMU), and the secondary sensor inputs are from Ultra-wideband system (UWB). This approach is validated using demonstration of comparison of computing load, and simulation results for accuracy and reliability testing using UAV flying mission scenario.