Cluster analysis and robust use of full-field models for sonar beamforming. - Tracey, Brian H.; Nigel Lee, Srinivas Turaga. (Tufts University. Tisch Library., 2017)
Multipath propagation in shallow water can lead to mismatch losses when single-path replicas are used for horizontal array beamforming. Matched field processing (MFP) seeks to remedy this by using full-field acoustic propagation models to predict the multipath arrival structure. Ideally, MFP can give source localization in range and depth as well as detection gains, but robustly estimating range and depth is difficult in practice. The approach described here seeks to collapse full-field replica outputs to bearing, which is robustly estimated, while retaining any signal gains provided by the full-field model. Cluster analysis is used to group together full-field replicas with similar responses. This yields a less redundant "sampled field" describing a set of representative multipath structures for each bearing. A detection algorithm is introduced that uses clustering to collapse beamformer outputs to bearing such that signal gains are retained while increases in the noise floor are minimized. Horizontal array data from SWELLEX-96 are used to demonstrate the detection benefits of sampled field as compared to single-path beamforming.