Wind Estimation and Fault Detection in Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) operate in complex atmospheric environments where wind disturbances such as gusts, shear layers and turbulence can degrade flight stability, reduce mission endurance and jeopardise safety. To maintain precise navigation and control, modern research combines sensor measurements (including airspeed probes, inertial units and GPS) with algorithmic estimators such as recursive filters and nonlinear observers. These techniques yield real-time estimates of wind vector components and dynamic pressure, allowing adaptive control laws to compensate aerodynamic disturbances. In parallel, fault detection and isolation methods monitor sensor and actuator health to ensure reliable attitude and position feedback. By generating residual signals and applying statistical or probabilistic reasoning, these methods quickly identify anomalies and reconfigure control loops to maintain safe flight. Together, advances in wind estimation and fault detection are enabling UAVs to perform energy-efficient path planning, resilient urban operations and autonomous inspection tasks with higher reliability and lower pilot intervention.

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Wind Estimation and Fault Detection in Unmanned Aerial Vehicles publication trend

The graph below shows the total number of articles in wind estimation and fault detection in unmanned aerial vehicles across all publications each year (not limited to Nature Index journals).

Technical terms

Kalman filter: A recursive algorithm that fuses sensor data and a dynamic model to estimate hidden states and uncertainties in real time.

Particle filter: A sequential Monte Carlo method that represents probability distributions by sampling numerous weighted hypotheses for state estimation.

Disturbance observer: An algorithm that infers external perturbations acting on a system by comparing predicted and measured responses.

Inertial measurement unit (IMU): A combined sensor module containing accelerometers, gyroscopes and often magnetometers for measuring motion and orientation.

Pitot tube: A pressure sensor mounted on an aircraft to measure dynamic pressure for airspeed determination.

References

1. Frequency-Based Wind Gust Estimation for Quadrotors Using a Nonlinear Disturbance Observer. IEEE Robotics and Automation Letters (2022).
2. Real-Time Wind Field Estimation and Pitot Tube Calibration Using an Extended Kalman Filter. Mathematics (2021).
3. A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis. Sensors (2021).