State Estimation of Fixed Wing Micro Air Vehicle through Cascaded Discrete Time Extended Kalman Filter (EKF) Schemes

MEMS based sensors have played a crucial role in the development navigation system for Micro Air Vehicle (MAV). As the Micro Air Vehicles (MAVs) are very small in size, therefore wind gust can highly affect their motion in air which makes it crucial to design an accurate Navigation system to properly guide and control the Micro Air Vehicle (MAV). On-board sensors included INS/GPS based navigation system which has MEMS based Gyroscope, MEMS based Magnetometer, MEMS based Accelerometer, and a GPS to find the position. These sensors are based on Micro Electro-Mechanical System (MEMS) technique and therefore, it is very effective for DARPA's weight and size limitation requirements. In this paper the states estimation (through inertial sensors) is carried out by using Different Extended Kalman Filter applied on Inertial Measurement Unit based on INS/GPS system which consists of Micro Electro-Mechanical (MEMS) Sensors. Three different algorithms for Discrete Time Cascaded Extended Kalman Filter are compared including a Single Stage Seven State Discrete Time Extended Kalman Filter, Two Stage Cascaded Discrete Time Extended Kalman Filter and Three Stage Cascaded Discrete Time Extended Kalman Filter. The Simulation results for three filters are compared and discussed in detail. Trajectory and sensors results are real time data, and a comparison is carried out for the MATLAB simulations which are obtained after applying Extended Kalman Filter (EKF) Schemes.