Multi-sensory data fusion for high performance attitude estimation

Abstract

Abstract In this paper, attitude angles estimation utilizing low power consumption and efficient approaches will be focused on. The trade-off between power consumption and efficiency is a leading factor in enhancing the overall performance of the navigation system in most of the autonomous system applications, so Complementary and Mahony filters as accelerometer, gyro, and magnetometer data fusion algorithms with high performance and less computation complexity are utilized. A real time implementation for Complementary and Mahony filters was achieved using raw data from Vector NAV VN-100 IMU.Estimated Euler angels using each filter were compared with Euler angels from Vector NAV VN-100 which are estimated using Kalman filter. A comparative study was carried out for analyzing the performance of each algorithm. The study presented that the Complementary filter introduces better efficiency than Mahony filter in case of stationary situations and steady flight. On the other hand, Mahony filter presents better efficiency than Complementary filter in case of motion and high dynamics.