Multi-Valued Eigen-Processing for Isolating Multiple Sources With a Rectangular Array

The multi-valued Bartlett (MVB) processor is useful for determining the locations of multiple acoustic sources in the ocean [J. Acoust. Soc. Am. 97, 235-241 (1995)]. This approach was originally applied to a vertical line array of hydrophones. The application to a rectangular array is explored here. The MVB processor is an eigen-processor that is based on the eigenvectors of the covariance matrix. It is multi-valued in the sense that an ambiguity surface is constructed for each member of a subset of the eigenvectors that correspond to the largest eigenvalues. The motivation for the approach is the fact that energy from different sources tends to partition into different eigenvectors. One of the advantages of the MVB processor on a rectangular array is that it is possible to determine if the partitioning is favorable without computing replica fields, which is often the most time-consuming task of matched-field processing computations. Examples are presented to illustrate the capabilities and limitations of the approach.

Automated summary

The MVB processor is a useful tool for determining the locations of multiple acoustic sources in the ocean, based on the eigenvectors of the covariance matrix. It allows for determining energy partitioning without the need for computing replica fields, making it advantageous for matched-field processing computations. Examples are presented to show the capabilities and limitations of the approach.