2020/06/23 by Sunsoo Kim, Kim, Sunsoo, Vaishnav Tadiparthi +3
Computer Science · Earth and Planetary Sciences · Engineering · #FOS: Electrical engineering #Geophysics and Gravity Measurements #Inertial Sensor and Navigation #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.14385
openalex publication_date 2020/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurate state estimation using low-cost MEMS (Micro Electro- Mechanical Systems) sensors present on Commercial-off-the-shelf (COTS) drones is a challenging problem. Most UAV systems use a combination of a gyroscope, an accelerometer, and a magnetometer to obtain measurements and estimate attitude. Under this paradigm of sensor fusion, the Extended Kalman Filter (EKF) is the most popular algorithm for attitude estimation in UAVs. In this work, we propose a novel estimation technique called extended H2 filter that can overcome the limitations of the EKF, specifically with respect to computational speed, memory usage, and root mean squared error. We formulate our attitude-estimation algorithm using unit quaternions. The H2 optimal filter gain is designed offline about a nominal operating point by solving a convex optimization problem, and the filter dynamics is implemented using the nonlinear system dynamics. This implementation of this H2 optimal estimator is referred as the extended H2 estimator. The proposed technique is tested on four cases corresponding to long time-scale motion, fast time-scale motion, transition from hover to forward flight for VTOL aircrafts, and an entire flight cycle (from take-off to landing). Its results are compared against that of the EKF in terms of the aforementioned performance metrics.