Nearfield Acoustic Holography using sparsity and compressive sampling principles

TL;DR

This paper introduces sparsity-based regularization and compressive sampling techniques for Near-field Acoustic Holography, enabling accurate source identification with fewer measurements and better handling of boundary discontinuities.

Contribution

It develops novel regularization schemes leveraging sparsity in a suitable basis and applies compressive sampling to reduce measurement requirements in NAH.

Findings

Effective handling of boundary velocity discontinuities.

Reduced number of microphones needed for accurate reconstruction.

Comparable or improved accuracy over standard methods.

Abstract

Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify the vibrating sources. This paper shows that, for convex homogeneous plates with arbitrary boundary conditions, new regularization schemes can be developed, based on the sparsity of the normal velocity of the plate in a well-designed basis, i.e. the possibility to approximate it as a weighted sum of few elementary basis functions. In particular, these new techniques can handle discontinuities of the velocity field at the boundaries, which can be problematic with standard techniques. This comes at the cost of a higher computational complexity to solve the associated optimization problem, though it remains easily tractable with out-of-the-box software. Furthermore, this sparsity framework allows us to take advantage of the concept of Compressive Sampling: under some conditions on the sampling process (here, the design of a random array, which can be numerically and experimentally validated), it is possible to reconstruct the sparse signals with significantly less measurements (i.e., microphones) than classically required. After introducing the different concepts, this paper presents numerical and experimental results of NAH with two plate geometries, and compares the advantages and limitations of these sparsity-based techniques over standard Tikhonov regularization.