Localizing Submerged Acoustic Sources Under Adverse Conditions

Abstract

This paper reviews various approaches for localizing submerged acoustic sources under adverse conditions. It is essential to obtain data of the highest possible quality when there are adverse conditions, such as uncertainties in the environment, source motion, and low signal-to-noise ratio. Focalization is an approach in which the source location and environmental parameters are treated as unknowns. Due to a parameter hierarchy in which source location outranks environmental parameters, there may be many realizations of the environment that bring the source into focus; the ambiguity in the environment can be an advantage if the primary objective is to localize the source. Environmental uncertainty is often associated with environmental complexity, which can be an advantage by reducing the ambiguity of the source location. Obtaining a high-quality estimate of the covariance matrix may be difficult when there is source motion, but the complexity of the received field from a moving source is another factor that can reduce ambiguity. It may be possible to localize a source that is buried in noise when an estimate of the noise covariance matrix is available. During the development of approaches for localizing submerged sources, much of the focus has been on one-dimensional vertical arrays. An extension of the multi-valued Bartlett processor to the case of a rectangular array was designed to take advantage of the extra dimension of the array and appears to have the potential to be the most powerful combination of hardware and signal processing to date.