A comparison of iterative deconvolution algorithms for the mapping of acoustic sources

Abstract

The DAMAS algorithm is compared with the DAMAS2 method and two other Fourier-based deconvolution approaches. One is the Richardson-Lucy method, which is widely used for the deconvolution of astronomical images. The second is a gradient-type NNLS approach, where spectral procedures are implemented to accelerate the computations. It is described how the DAMAS2 and the Fourier-based NNLS algorithms can be embedded in an outer iteration loop to take the variation of the point-spread function into account. All methods are tested with synthetic data. At first an example with a simple linear array and a small opening angle is considered, where the variation of the point-spread function in the source region is negligible. In this test case the results of the DAMAS2, the Fourier-based NNLS, the Richardson-Lucy, and the original DAMAS algorithm are compared. It is shown that all algorithms more or less introduce oscillations in the reconstructed source distribution. The effect of background noise on the results is investigated. A second test case with a planar array and a large opening angle is presented, to demonstrate the influence of a strong variation of the point-spread function. It is shown that the methods which neglect the variation of the point-spread function lead to distorted results, while the embedded Fourier-based methods and the DAMAS algorithm give a significantly better reconstruction of the source distribution.