  The Low frequency analysis and recording (LOFAR) spectrum is one of the key
features of the under water target, which can be used for underwater target
recognition. However, the underwater environment noise is complicated and the
signal-to-noise ratio of the underwater target is rather low, which introduces
the breakpoints to the LOFAR spectrum and thus hinders the underwater target
recognition. To overcome this issue and to further improve the recognition
performance, we adopt a deep learning approach for underwater target
recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater
target recognition scheme, which consists of preprocessing, offline training,
and online testing. In preprocessing, a LOFAR spectrum enhancement based on
multi-step decision algorithm is specifically designed to recover the
breakpoints in LOFAR spectrum. In offline training, we then adopt the enhanced
LOFAR spectrum as the input of convolutional neural network (CNN) and develop a
LOFAR-based CNN (LOFAR-CNN) for online recognition. Taking advantage of the
powerful capability of CNN in feature extraction, the proposed LOFAR-CNN can
further improve the recognition accuracy. Finally, extensive simulation results
demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of
$95.22\%$, which outperforms the state-of-the-art methods.
