In this paper, we explore the use of optical correlation-based recognition to identify and position underwater man-made objects (e.g. mines). Correlation techniques can be defined as a simple comparison between an observed image (image to recognize) and a reference image; they can be achieved extremely fast. The result of this comparison is a more or less intense correlation peak, depending on the resemblance degree between the observed image and a reference image coming from a database. However, to perform a good correlation decision, we should compare our observed image with a huge database of references, covering all the appearances of objects we search. Introducing all the appearances of objects can influence speed and/or recognition quality. To overcome this limitation, we propose to use composite filter techniques, which allow the fusion of several references and drastically reduce the number of needed comparisons to identify observed images. These recent techniques have not yet been exploited in the underwater context. In addition, they allow for integrating some preprocessing directly in the correlation filter manufacturing step to enhance the visibility of objects. Applying all the preprocessing in one step reduces the processing by avoiding unnecessary Fourier transforms and their inverse operation. We want to obtain filters that are independent from all noises and contrast problems found in underwater videos. To achieve this and to create a database containing all scales and viewpoints, we use as references 3D computer-generated images.