Wavefront Adaptive Sensing Beamformer for Shallow-Water Broadband Interference Mitigation

This paper addresses robust adaptive beamforming for passive sonar in shallow-water environments. In these complex multipath environments, adaptive beamformers typically suffer from signal self-cancellation. Existing approaches to robust adaptive beamforming attempt to model and compensate for the uncertainty in the beamformer's hypothesized signal subspace by using additional constraints. However, these constraints reduce the adaptivity of the beamformer and make it prone to insufficiently suppressing interference. In this paper, blind source separation methods are used to adaptively estimate the spatial wavefronts of both targets and interference without requiring physical modeling of the channel. By exploiting the different temporal spectra of targets and interference, this method constructs a "signal-free" covariance matrix without imposing directional gain constraints. By constructing the "signal-free" covariance in this way, the wavefront adaptive sensing (WAS) beamformer is able to sufficiently suppress interference other adaptive beamformers can not. Real data results, using data from the SWellEx96 S59 event, show that the proposed WAS beamformer achieves a higher output signal-to-interference-plusnoise ratio (SINR) on average than conventional and minimum variance (MV) adaptive beamformers.