Particle velocity estimation based on a two-microphone array and Kalman filter.

Area of Science:

• Acoustics
• Sensor Technology
• Signal Processing

Background:

• Traditional particle velocity measurement using finite difference (FD) methods with pressure microphones is susceptible to sensor noise and mismatch.
• Microflown sensors, based on micro-electro-mechanical systems (MEMS) technology, offer improved robustness over FD methods.
• There is a need for robust, cost-effective particle velocity measurement techniques that do not rely on specialized fabrication.

Purpose of the Study:

• To develop and validate a novel two-microphone approach, termed the u-sensor, for robust particle velocity measurement.
• To demonstrate that the u-sensor can achieve high performance using ordinary microphones and robust adaptive filtering techniques.
• To compare the performance of the u-sensor against traditional FD methods and the Microflown sensor.

Main Methods:

• The u-sensor method employs robust adaptive filtering, incorporating plane and spherical wave models.
• A Kalman filter is formulated, explicitly accounting for process and measurement noise.
• The technique utilizes two standard pressure microphones, avoiding the need for novel sensor fabrication.

Main Results:

• Numerical and experimental investigations validated the proposed u-sensor technique.
• The u-sensor demonstrated superior performance compared to the conventional finite difference (FD) method.
• The performance of the u-sensor was found to be comparable to that of the Microflown sensor.

Conclusions:

• The u-sensor provides a robust and effective method for particle velocity measurement using readily available microphones.
• This approach overcomes the limitations of traditional FD methods concerning noise and sensor mismatch.
• The u-sensor presents a viable alternative to existing advanced sensors, offering comparable performance without specialized manufacturing requirements.