Matched beam-intensity processing for a deep vertical line array

Abstract

A vertical line array can be deployed in deep water below the critical depth, the depth where the sound speed equals the sound speed at the surface, to take advantage of the lower ambient noise level (compared with above the critical depth) for target detection. To differentiate a submerged source from a surface source, a Fourier transform based method [McCargar and Zurk, J. Acoust. Soc. Am. 133, EL320–325 (2013)] was proposed for a narrowband signal that exploits the depth-related harmonic (oscillation) feature of the beam power time series associated with the target arrival. In this paper, incoherent matched beam processing is used to estimate the target depth. Where the replica (calculated) beam intensity or amplitude time series best matches that of the data is used to estimate the source depth. This method is shown, based on simulated data, to provide a better depth resolution in general and better ability to estimate the depth of a very shallow source (say at 10 m) and can be used to complement the Fourier transform based method. It can be extended to process (random) broadband signals and to environments where the Lloyd's mirror theory is not valid.

Underwater source localisation utilising interference pattern under low SNR conditions

“…The three dimensions of X represent the source frequency f , the range r , and the arrival grazing angle θ respectively. The slice of the interference tensor along frequency, which is described as the angle‐range interference pattern [12] as shown in Figure 3b, has a similar periodic structure with peak stripes. The slice of the interference tensor along the grazing angle dimension, which is described as the frequency‐distance interference pattern after beamforming [14], is shown in Figure 3c.…”

“…McCargar and Zurk [11] modelled the variation of the signal beam intensity as a function of range and explained the harmonic feature of the peak beam power as a result of Lloyd's mirror interference, the range-grazing angle (beam) interference pattern is first proposed in this paper. Zhou and Zheng [12,13] processed the Vertical Line Array (VLA) data with incoherent matched beam processing to estimate the source depth. Yang [14] used the beam angle dependence of the striations between the beam spectrograms to suppress interference contribution.…”

Target depth estimation by deep neural network based on acoustic interference structure in deep water

“…However, due to the low target strength of a quiet target, multipath arrivals are hardly extracted under the low signal-to-noise or the signal-toreverberation ratio conditions. The coherent addition of the D and SR arrivals is enhanced by RAP and results in interference patterns in the intensity plot, which has been applied to the passive depth estimation [3,[11][12][13][14]. A robust estimate can be obtained by integrating the intensity plot over a sufficiently long time.…”