Low-cost MEMS sensors are widely utilized in UAV platforms to address attitude estimation problems due to their compact size, low power consumption, and cost-effectiveness. Diverse UAV payloads pose new challenges for attitude estimation, such as magnetic interference environments and high dynamic environments. In this paper, we propose a hierarchical decoupled attitude estimation algorithm, termed HDAEA. Initially, a novel hierarchical decoupling approach is introduced for the attitude and angle representation of the direction cosine matrix, enabling the representation of angles in a new manner. This method reduces the data dimensionality and nonlinearity of observation equations. Furthermore, a magnetic interference identification algorithm is proposed to compute the magnetic interference intensity accurately and quantitatively. Combining the quantified errors of estimated state variables, an error model for magnetic interference and attitude angles in high-dynamic environments is constructed. Subsequently, the proposed error model is employed to calibrate the hierarchical decoupled angles using accelerometer and magnetometer measurements, effectively mitigating the impact of magnetic interference on the calculation of pitch angles and roll angles. Moreover, the integration of the proposed hierarchical decoupled attitude estimation algorithm with the error-state extended Kalman filter reduces system nonlinearity and minimizes linearization errors. Experimental results demonstrate that HDAEA exhibits significantly improved attitude estimation accuracy of UAV payloads.
Keywords: MEMS; attitude estimation; error-state extended Kalman filter (ESKF); magnetic interference; multifunctional payloads; orientation decoupling.