Sensor Fusion based on Complementary Algorithms using MEMS IMU

Conventional attitude/orientation estimating filters are generally complex demanding excessive computational burden. This instigates the requirement for a computationally simple yet sufficiently precise algorithm for applications where computational complexity is of prime import. A relatively simple, robust and equally efficient technique in this regard is the development of complementary filters. Gradient Descent based Complementary Algorithm (GDCA) and Explicit Complementary Clgorithm (ECA) are the latest advancement in complementary filters applicable to low cost, low power MEMS based inertial measurement units (IMUs) employing quaternion. These fixed gain estimators employ gyroscope triad for high frequency estimation and accelerometer triad for low frequency attitude estimation. This paper appraises the performance of GDCA and ECA. Both simulation and experimental results are presented for comparative analysis. Simulated data was generated in MATLAB for known orientation in term of Euler angles to validate the filters performance whereas for practical implementation of different scenarios, MEMS based MPU6050 IMU was employed. As only IMU is employed without aided sensory system, the mandate of this research is limited to attitude estimation in terms of Euler roll and pitch angles. Roll of the adjustable filter gains is also assessed for a range of values.