{ "id": "1301.0237", "version": "v2", "published": "2013-01-02T14:13:39.000Z", "updated": "2014-04-03T15:42:43.000Z", "title": "Sampling and reconstruction of solutions to the Helmholtz equation", "authors": [ "Gilles Chardon", "Albert Cohen", "Laurent Daudet" ], "categories": [ "math.NA" ], "abstract": "We consider the inverse problem of reconstructing general solutions to the Helmholtz equation on some domain $\\Omega$ from their values at scattered points $x_1,\\dots,x_n\\subset \\Omega$. This problem typically arises when sampling acoustic fields with $n$ microphones for the purpose of reconstructing this field over a region of interest $\\Omega$ contained in a larger domain $D$ in which the acoustic field propagates. In many applied settings, the shape of $D$ and the boundary conditions on its border are unknown. Our reconstruction method is based on the approximation of a general solution $u$ by linear combinations of Fourier-Bessel functions or plane waves. We analyze the convergence of the least-squares estimates to $u$ using these families of functions based on the samples $(u(x_i))_{i=1,\\dots,n}$. Our analysis describes the amount of regularization needed to guarantee the convergence of the least squares estimate towards $u$, in terms of a condition that depends on the dimension of the approximation subspace, the sample size $n$ and the distribution of the samples. It reveals the advantage of using non-uniform distributions that have more points on the boundary of $\\Omega$. Numerical illustrations show that our approach compares favorably with reconstruction methods using other basis functions, and other types of regularization.", "revisions": [ { "version": "v2", "updated": "2014-04-03T15:42:43.000Z" } ], "analyses": { "keywords": [ "helmholtz equation", "reconstruction method", "acoustic field propagates", "linear combinations", "fourier-bessel functions" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }