On-grid compressive sampling for spherical field measurements in acoustics

We derive a compressive sampling method for acoustic field reconstruction using field measurements on a pre-defined spherical grid that has theoretically guaranteed relations between signal sparsity, measurement number, and reconstruction accuracy. This method can be used to reconstruct band limited spherical harmonic or Wigner D-function series (spherical harmonic series are a special case) with sparse coefficients. Contrasting typical compressive sampling methods for Wigner D-function series that use arbitrary random measurements, the new method samples randomly on an equiangular grid, a practical and commonly used sampling pattern. Using its periodic extension, we transform the reconstruction of a Wigner D-function series into a multi-dimensional Fourier domain reconstruction problem. We establish that this transformation has a bounded effect on sparsity level and provide numerical studies of this effect. We also compare the reconstruction performance of the new approach to classical Nyquist sampling and existing compressive sampling methods. In our tests, the new compressive sampling approach performs comparably to other guaranteed compressive sampling approaches and needs a fraction of the measurements dictated by the Nyquist sampling theorem. Moreover, using one-third of the measurements or less, the new compressive sampling method can provide over 20 dB better de-noising capability than oversampling with classical Fourier theory.

Automated summary

We propose a compressive sampling method for reconstructing acoustic fields based on field measurements on a pre-defined spherical grid. This method establishes the relations between signal sparsity, measurement number, and reconstruction accuracy. In comparison to traditional methods, the proposed method uses equiangular grid sampling and transforms the reconstruction problem into a multi-dimensional Fourier domain problem. Experimental results show that this method outperforms classical Nyquist sampling and requires fewer measurements.