Deep learning based technique for enhanced sonar imaging

Several beamforming techniques can be used to enhance the resolution of sonar images. Beamforming techniques can be divided into two types: data independent beamforming such as the delay-sum-beamformer, and data-dependent methods known as adaptive beamformers. Adaptive beamformers can often achieve higher resolution, but are more sensitive to errors. Several signals are processed from several consecutive pings. The signals are added coherently to achieve the same effect as having a longer array in synthetic aperture sonar (SAS). In general it can be said that a longer array gives a higher image resolution. SAS processing typically requires high navigation accuracy, and physical array-overlap between pings. This restriction on displacement between pings limits the area coverage rate for the vehicle carrying the SAS. We investigate the possibility to enhance sonar images from one ping measurements in this paper. This is done by using state-of-the art techniques from Image-to-Image translation, namely the conditional generative adversarial network (cGAN) Pix2Pix. The cGAN learns a mapping from an input to output image as well as a loss function to train the mapping. We test our concept by training a cGAN on simulated data, going from a short array (low resolution) to a longer array (high resolution). The method is evaluated using measured SAS-data collected by Saab with the experimental platform Sapphires in freshwater Lake Vättern.QC 20191112SMaR