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Attitude estimation is the process of computing the orientation angles of anobject with respect to a fixed frame of reference. Gyroscope, accelerometer,and magnetometer are some of the fundamental sensors used in attitudeestimation. The orientation angles computed from these sensors are combinedusing the sensor fusion methodologies to obtain accurate estimates. Thecomplementary filter is one of the widely adopted techniques whose performanceis highly dependent on the appropriate selection of its gain parameters. Thispaper presents a novel cascaded architecture of the complementary filter thatemploys a nonlinear and linear version of the complementary filter within oneframework. The nonlinear version is used to correct the gyroscope bias, whilethe linear version estimates the attitude angle. The significant advantage ofthe proposed architecture is its independence of the filter parameters, therebyavoiding tuning the filters gain parameters. The proposed architecture does notrequire any mathematical modeling of the system and is computationallyinexpensive. The proposed methodology is applied to the real-world datasets,and the estimation results were found to be promising compared to the otherstate-of-the-art algorithms.

Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation