Parameter Estimation for Flight Energy Consumption Models using Kalman Filtering

This paper presents the implementation of Kalman filtering for estimating key aerodynamic parameters governing flight energy consumption models for multirotor Unmanned Aerial Vehicles (UAVs). Particularly, the proposed method focuses on identifying the combined aerodynamic drag parameters that directly influence power and energy usage during sustained flight. The proposed approach employs a state-space representation of three-dimensional vehicle dynamics under level-flight conditions while explicitly accounting for the influence of ambient wind disturbances. Sensor measurements are assumed to originate from the standard onboard sensing suite commonly available on multirotor platforms, including inertial and kinematic information. The estimation framework is evaluated in a simulated environment with realistic measurement noise and environmental uncertainties. Simulation results demonstrate the effectiveness of the proposed Kalman filter in accurately tracking aerodynamic parameters, highlighting its potential for improving energy-aware modeling, prediction, and control of UAVs.