Underwater Broadband Target Detection by Filtering Scanning Azimuths Based on Features of Subband Peaks

The postprocess is widely adopted in passive sonar to improve the performance of target detection by raising the bearing resolution and suppressing the background noise, whereas the conventional algorithms separately adopted on the subbands always perform poorly when detecting weak target. To solve this problem, this article proposes a method of filtering scanning azimuths for single target (FSAS) for uniform line array (ULA) based on the features of subband peaks. The proposed method enhances the subband peaks through the energy selection, modified Eckart filter, and noise weighting, wherein the scanning azimuths are filtered based on the accumulated energy and count features to finally preserve those near the target azimuth, which improves the normalized azimuth spectrum for better detection effect. This approach is validated by both simulated and measured data and provides an idea for detecting the weak target submerged by the strong background interferences.