  Attitude estimation is the process of computing the orientation angles of an
object with respect to a fixed frame of reference. Gyroscope, accelerometer,
and magnetometer are some of the fundamental sensors used in attitude
estimation. The orientation angles computed from these sensors are combined
using the sensor fusion methodologies to obtain accurate estimates. The
complementary filter is one of the widely adopted techniques whose performance
is highly dependent on the appropriate selection of its gain parameters. This
paper presents a novel cascaded architecture of the complementary filter that
employs a nonlinear and linear version of the complementary filter within one
framework. The nonlinear version is used to correct the gyroscope bias, while
the linear version estimates the attitude angle. The significant advantage of
the proposed architecture is its independence of the filter parameters, thereby
avoiding tuning the filters gain parameters. The proposed architecture does not
require any mathematical modeling of the system and is computationally
inexpensive. The proposed methodology is applied to the real-world datasets,
and the estimation results were found to be promising compared to the other
state-of-the-art algorithms.
