Sparse regularization has been successfully applied to equivalent source method (ESM) in order to improve the acoustic imaging resolution. However, the application is not always feasible, especially at low frequencies. To overcome the problem, this paper proposes a high-resolution acoustic imaging method. In this method, reweighted l₁ minimization is introduced to ESM to deal with the ill-posed inverse problems. Then the obtained equivalent source strengths are used to locate the sound sources. Compared to the sparse regularization-based ESM, the proposed method can provide a low side lobe and higher spatial resolution of acoustic imaging. Meanwhile, by arranging equivalent sources in three-dimensional space, the proposed method can also realize the acoustic imaging in three-dimensional sound field with high resolution. The results of the simulation and experiment demonstrate the validations.

The special structural design of acoustic metamaterials further extends acoustic properties of the materials. We design a tunable acoustic passive phased array based on double-opening resonant rings, which modulates the acoustic waves only by the rotational angle, making up for the defect of the fixed structure of ordinary metamaterials. The rotation angle is selected based on the generalized Snell’s law, which not only enables focusing in a large frequency band range but also meets the focusing demand of acoustic waves incident at different angles and controls the position of the focal point.

In this paper, a water-based aerosol particle counter was used to measure aerosol number concentrations with high temporal resolution at a meteorological tower and on the ground, and the ultrasonic anemometer on the meteorological tower measured the data of the three-dimensional wind speed. The power spectrum of the aerosol particle number concentration fluctuation was obtained by using a Fourier transform, and the characteristics of the power spectrum were deeply analyzed. The results show that the aerosol concentration fluctuation power spectrum satisfies the Monin–Obukhov law in the low-frequency (0.02–0.25 Hz) part of the inertial subregion, which is consistent with the characteristics of atmospheric turbulent motion. Significant attenuation occurs in the high-frequency (0.3–5 Hz) range, which is mainly caused by the attenuation of the aerosol concentration by the intake pipe. Using the similarity of the power spectrum in the low-frequency part, using the “−5/3” line as a standard, the characteristic time of the measurement system is obtained by fitting the transfer function. The results show that in the flux measurement experiments in this paper, the characteristic time is usually less than 1 s. Finally, this paper uses the Fourier transform and wavelet transform to correct the high-frequency attenuation in the fluctuation of aerosol concentration and obtains the corrected aerosol flux. The results show that the effect of high-frequency attenuation on the flux is approximately 1–4% in this experiment.

Sound field separation techniques are an advancing tool for extracting a target sound field from a mixed sound field. However, the methods bear a high measurement cost due to the restriction of the sampling theorem. In this study, a sound field separation method based on sparse sampling is established. The method initially utilizes dictionary learning to generate a sparse basis of the sound field. Then, a mixed sound field can be precisely recovered from sparse sampling of sound pressure and the target sound field can be extracted based on the recovered sound field by means of the theory of equivalent source method. The method is validated by numerical simulations. Compared to sound field separation based on the equivalent source method, the proposed method has advantage in terms of both the accuracy and the stability for sparse sampling.