Attitude estimation by using MEMS IMU with Fuzzy Tuned Complementary Filter

This paper presents a novel tuning method for complementary filter exploited for attitude estimation. The complementary filter is the choice of the day in fields where computational simplicity, low power consumption and low cost involved are of prime significance with little interest in the highest degree of accuracy. It is well-known that attitude estimation based on gyroscope measurement alone quickly diverges due to inherent bias issue which is compensated by accelerometer using filtering algorithms. Conventional Complementary Filters (CCF) employed for attitude estimation have fixed filters gain which makes them impassive to the dynamic situation through which the system undergoes, resulting in erroneous estimations in such case. The more complex algorithms have been employed at the cost of computational complexity but are not suitable for most applications based on simple approach and resources. In this paper, Fuzzy Tuned Complementary Filter (FTCF) is proposed to eradicate this issue with the obvious benefit of least computational burden. The proposed algorithm is appraised and validated in conjunction with the well-established Kalman filter using MEMS-based IMU.