A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays

Abstract

Micro-electronic-mechanical system (MEMS) is widely used in various applications, especially as a low-cost and small size system for attitude estimation which requires high accuracy and fast response. This work proposes a novel decision-tree based multiple-model unscented Kalman filter (DTMM-UKF) for attitude estimation. It is a quaternion-based attitude estimator that fuses related strap-down magnetic, angular rate, and gravity (MARG) sensor arrays. A set of novel criteria for testing whether the magnetometer and accelerometer are reliable is developed. To improve the anti-interference performance, we define four different filter models for the UKF. Particularly, a decision tree is established to automatically switch filter model based on these reliability test criteria. The priori attitude estimation is obtained from the process model using gyroscope data. Fusing the accelerometer and magnetometer data together, the observation attitude could be solved based on corresponding objective function and Jacobian matrix determined by the filter model. Under the UKF frame, the final optimal attitude could be determined by fusing priori estimation and observed attitude. Experimental tests show that the DTMM-UKF algorithm has better robustness and higher real-time estimation accuracy.