Abstract

Broadband synthetic aperture sonar (SAS) is a high resolution underwater imaging technique, which uses a digital matched filter for pulse compression followed by aperture synthesis, also referred to as azimuthal matched filtering, to improve resolution. Thus, the processing scheme is equivalent to a two-dimensional matched filter operation, in which the point spread function (PSF) for the particular SAS-geometry considered is correlated with the observation. It can be shown that this processing scheme is suboptimal, because it causes a blurring of the processed image. Therefore, the purpose of this paper is to develop a computationally efficient iterative algorithm for reconstruction, which compares effectively to the matched filter in the processing time, but shows significant improvement in image detail. The proposed iterative restoration algorithm is derived by modifying the optimal gradient method through an adaptive relaxation technique. The adaptivity is introduced to incorporate properties of the true restoration error. This makes the successive approximations approach the exact solution much faster, enabling a visually good convergence within a few iterates. Asymptotic convergence of the proposed iterative algorithm is established. For the experimental results which are shown, the new algorithm performs better on accuracy and compares well on computation time. Application to underwater object imaging using simulated data shows clear improvements compared to a matched filter processing technique.