Multi-sensor fusion based state estimation for UAV

Abstract

Unmanned Aerial Vehicle (UAV) is a device capable of flying in the air. It is very popular in a wide range of industries and it is capable of carrying out different tasks. State estimation is required for autonomous operations of UAVs. There are several methods for state estimation, with sensor fusion based state estimation being one of them. One of the uses of state estimation is for UAV localisation. This paper presents a sensor fusion based state estimation using Extended Kalman Filter (EKF) algorithm for localisation of a UAV. Based on the distance measurements, IMU data and GPS data from the quadcopter, the EKF is used for state estimation and is implemented to obtain the estimated position of the quadcopter. Simulation results shows that Global Positioning System (GPS) and Inertial Measurement Unit (IMU) fusion is able to provide a precise and reliable localisation.