INS/GPS Based State Estimation of Micro Air Vehicles Parametric Study Using Extended Kalman Filter (EKF) Schemes

Abstract

Micro Air Vehicles (MAVs) are classified as the small scale aircrafts which can be remote controlled, semi-autonomous and autonomous. They are highly sensitive to the wind gust and therefore the control of Micro Air Vehicle is a very challenging area. To control an aircraft, the first step is the precise navigation of the MAV and state estimation is the pre requisite. In this paper the states (roll, pitch, yaw, position and wind direction) are estimated with the help of Discrete Time Extended Kalman Filter operating on Inertial Measurement Unit (IMU) which consists of MEMS Gyro, MEMS Accelerometer, MEMS Magnetometer and GPS. These sensors are based on MEMS (Micro Electro Mechanical System) technique which is very helpful for size and weight reduction. Three techniques of Discrete Time Extended Kalman Filter are used named as Single state Discrete Time Extended Kalman, Cascaded Two Stage Discrete Time Extended Kalman Filter and Cascaded Three Stage Discrete Time Extended Kalman Filter. The simulations obtained from these filters are compared and analyzed. Trajectory and sensors data is recorded from Flight Simulator software, and then compared with the simulations obtained from Extended Kalman Filter with the help of MATLAB Software.