Microphones as Airspeed Sensors for Unmanned Aerial Vehicles

Area of Science:

• Aerospace Engineering
• Fluid Dynamics
• Instrumentation

Background:

• Accurate airspeed measurement is critical for unmanned aerial vehicle (UAV) flight control and safety.
• Traditional pitot tubes can be vulnerable to damage and icing, especially on small UAVs.
• Turbulent boundary layer pressure fluctuations offer a potential alternative sensing mechanism.

Purpose of the Study:

• To develop and validate a novel airspeed instrument for small fixed-wing tail-sitter UAVs.
• To explore the relationship between wall-pressure fluctuations and airspeed.
• To utilize a neural network for real-time airspeed computation.

Main Methods:

• Designing an instrument with two microphones to capture pseudo-sound from the turbulent boundary layer.
• Employing a feed-forward single-layer neural network to process microphone signals.
• Training and validating the neural network using wind tunnel and flight experimental data.

Main Results:

• The developed instrument successfully relates power spectra of wall-pressure fluctuations to airspeed.
• The best neural network model achieved a mean approximation error of 0.043 m/s and a standard deviation of 1.039 m/s using flight data.
• Airspeed prediction remains accurate across a range of angles of attack when the angle is known.

Conclusions:

• The proposed instrument offers a viable, novel method for airspeed measurement in small UAVs.
• Neural network processing of pressure fluctuation data is effective for airspeed estimation.
• The system's performance is robust, with known angle of attack enabling accurate measurements.