Attitude Determination for MAVs Using a Kalman Filter

Abstract

This paper presents a Kalman filter to effectively and economically determine the Euler angles for micro aerial vehicles (MAVs), whose size and payload are severely limited. The filter uses data from a series of micro-electro mechanical system sensors to determine the selected 3 variables of the direction cosine matrix and the bias of the rate gyro sensors as state elements in a dynamic model, with the gravitational acceleration to build a measurement model. For high speed maneuvers, rigid motion equations are used to correct the measurements of the gravitational acceleration. The filter is designed to automatically tune its gain based on the dynamic system state. Simulations indicate that the Euler angles can be determined with standard deviations less than 3°. The algorithm was successfully implemented in a miniature attitude measurement system suitable for MAVs. Aerobatic flights show that the attitude determination algorithm works effectively. The attitude determination algorithm is effective and economical, and can also be applied to bionic robofishs and land vehicles, whose size and payload are also greatly limited.