Optimal time-domain beamforming with simulated annealing including application of a p r i o r i information

Abstract

A fast simulated annealing algorithm is developed for an optimal time-domain beamformer. The optimal beamformer has greater resolution than the standard frequency-domain beamformers, which discard information by averaging the data to form a correlation matrix and remove degrees of freedom by collapsing the number of unknown parameters. The optimal ambiguity function uses the data in raw form and depends on all of the unknown source parameters. This approach is practical with simulated annealing. A specialized simulated annealing algorithm is required to search for the source bearings and time series because the parameters are analogous to a mixture of substances with different freezing points. Examples are presented to demonstrate that the optimal beamformer can be enhanced significantly with a priori information and to illustrate the effects of source level and bandwidth, noise level, array size, and number of sources.

Maximum Likelihood Deconvolution of Beamformed Images with Signal-Dependent Speckle Fluctuations from Gaussian Random Fields: With Application to Ocean Acoustic Waveguide Remote Sensing (OAWRS)

“…Past statistical models for acoustic beamforming, including past maximum likelihood approaches, in the ocean are not well suited to OAWRS applications, because they have overwhelmingly assumed a deterministic signal model in additive Gaussian noise with a priori knowledge of the noise's spatial correlation, which is most often assumed to be spatially uncorrelated for horizontal apertures' element spacing exceeding half the wavelength [16][17][18][19][20][21]. Neither of these assumptions is consistent with those observed in OAWRS sensing, where both signal and noise fields are random and highly correlated in space across the array due to their far field origins.…”

“…Neither of these assumptions is consistent with those observed in OAWRS sensing, where both signal and noise fields are random and highly correlated in space across the array due to their far field origins. Additionally, it has been noted [25] that some persistent confusion and misidentification has occurred between maximum likelihood [22,23] and other methods [19][20][21] in beamforming applications.…”

“…We focus on OAWRS applications [4][5][6]11,13,14] where side lobes are sufficiently low such that distinct beams are statistically independent of each other and far field signal and noise sources dominate. Many past statistical models for beamforming in the ocean [16][17][18][19][20][21] are less well suited to OAWRS applications, because they assume deterministic signals that are inconsistent with observations and additive noise with spatial correlations that are inconsistent with nonuniform far field origins. While the approach presented here is general, a line array geometry is used in examples both for simplicity and because line arrays are typically used in OAWRS applications.…”

Angular Resolution Enhancement Provided by Nonuniformly-Spaced Linear Hydrophone Arrays in Ocean Acoustic Waveguide Remote Sensing

“…Large aperture densely-sampled arrays of sensors are employed in remote sensing and communication applications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] to enhance signal-to-noise ratio (SNR) via coherent beamforming [17][18][19][20][21][22], which reduces noise coming from directions outside the signal beam. Coherent beamforming also provides an estimate of signal bearing, required for direct spatial localization of a signal [11,12,[23][24][25].…”

Continental-shelf scale Passive Ocean Acoustic Waveguide Remote Sensing of marine mammals and other submerged objects including detection, localization, and classification

“…The vocalization source level distributions are based on measurements made using a large-aperture densely-sampled coherent hydrophone array system that provides high SNR in signal detection, large sample sizes, as well as robust array-based methods for whale localization using vocalization bearing-time measurements over areas spanning 100,000 km Large aperture densely-sampled arrays of sensors are employed in remote sensing and communication applications [4, 14, 16-19, 65, 68, 77, 140, 150-155] to enhance signal-to-noise ratio (SNR) via coherent beamforming [13,27,70,71,156,157], which reduces noise coming from directions outside the signal beam. Coherent beamforming also provides an estimate of signal bearing, required for direct spatial localization of a signal [14,[66][67][68]158].…”