Nearfield acoustic holography in a moving medium based on particle velocity input using nonsingular propagator

Nearfield acoustic holography in a moving medium is a technique which is typically suitable for sound sources identification in a flow. In the process of sound field reconstruction, sound pressure is usually used as the input, but it may contain considerable background noise due to the interactions between microphones and flow moving at a high velocity. To avoid this problem, particle velocity is an alternative input, which can be obtained by using Laser Doppler Velocimetry in a non-intrusive way. However, there is a singular problem in the conventional propagator relating the particle velocity to the pressure, and it could lead to significant errors or even false results. In view of this, in this paper nonsingular propagators are deduced to realize accurate reconstruction in both cases that the hologram is parallel to and perpendicular to the flow direction. The advantages of the proposed method are analyzed, and simulations are conducted to verify the validation. The results show that the method can overcome the singular problem effectively, and the reconstruction errors are at a low level for different flow velocities, frequencies, and signal-to-noise ratios.

Sensor placement for sound field reconstruction in enclosures.

Sampling spatio-temporal acoustic fields is a challenging problem since it demands a large number of sensors. Typically, to characterize the pressure field inside an enclosure, the number of measurements required increases linearly with frequency and cubically with volume, becoming an intractable problem for rooms of moderate size even at low and mid frequencies. Sparse representation techniques, such as Compressed Sensing, rely on the sparsity of natural signals in certain representation domain to drastically reduce the number of measurements needed to sample such signals. In this study, we optimize the placement of sensors inside an enclosure in order to reduce the measurements required for a given reconstruction accuracy. The proposed methodology selects a sparse set of sensor positions from predefined grid via the QR factorization of the sensing matrix. Numerical results show an effective reduction in the required number of measurements when their positions are optimized, in contrast to standard random positioning. Unlike the majority of existing approaches, we study the placement problem for wide-band acoustic fields.