Distributed Estimation and Fusion for Multi-Quadcopter Wind Field Estimation
Abstract
Wind estimation can be widely used in the planning and control of uncrewed aerial systems and for meteorology and environmental studies. Fusion of wind estimates from multiple quadcopters can result in improved accuracy of wind estimation that may not be attainable by a single quadcopter. In this article, we consider a multi-quadcopter wind field estimation problem. We design an invariant extended Kalman filter (IEKF) and integrate it with a sequential covariance intersection (SCI) algorithm to fuse wind estimates from multiple quadcopters. We also develop baseline algorithms such as a least-squares method and a multiplicative-EKF (MEKF) SCI algorithm. The effectiveness of the proposed IEKF-SCI algorithm is compared with the baseline algorithms and demonstrated in simulations under various wind field models.
Fields of Study & Subjects
Aerospace and Aviation Technology
Air Traffic Management and Optimization
Meteorological Phenomena and Simulations
Publication details
- Journal / source
- IEEE Transactions on Control Systems Technology
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 34
- Issue
- 2
- Pages
- 934–946
- Publication date
- 1 January 2026