Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

TL;DR

This paper introduces a novel cascaded complementary filter architecture for sensor fusion in attitude estimation that eliminates the need for parameter tuning and is computationally efficient, improving accuracy over existing methods.

Contribution

It proposes a new cascaded filter architecture combining nonlinear and linear filters, removing the need for gain parameter tuning in attitude estimation.

Findings

The architecture effectively corrects gyroscope bias.

It achieves accurate attitude estimation without system modeling.

Results outperform state-of-the-art algorithms on real datasets.

Abstract

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.