Midfrequency “Through-the-Sensor” Scattering Measurements: A New Approach

Brown, Walter E.; Barlett, Martin L

doi:10.1109/joe.2005.862088

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…A recent through-the-sensor remote-sensing technique for acoustic-parameter estimation has been referred to as SABLE (sonar active boundary loss estimation) [139]. Bottom scattering strengths were derived from active hull-mounted naval sonar data by using eigenray paths modeled by CASS and GRAB to associate sonar-signal attributes with specific propagation paths.…”

Section: Acoustic Transmission Optionsmentioning

confidence: 99%

Advanced Applications for Underwater Acoustic Modeling

Etter

2012

Advances in Acoustics and Vibration

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Changes in the ocean soundscape have been driven by anthropogenic activity (e.g., naval-sonar systems, seismic-exploration activity, maritime shipping and windfarm development) and by natural factors (e.g., climate change and ocean acidification). New regulatory initiatives have placed additional restrictions on uses of sound in the ocean: mitigation of marine-mammal endangerment is now an integral consideration in acoustic-system design and operation. Modeling tools traditionally used in underwater acoustics have undergone a necessary transformation to respond to the rapidly changing requirements imposed by this new soundscape. Advanced modeling techniques now include forward and inverse applications, integrated-modeling approaches, nonintrusive measurements, and novel processing methods. A 32-year baseline inventory of modeling techniques has been updated to reflect these new developments including the basic mathematics and references to the key literature. Charts have been provided to guide soundscape practitioners to the most efficient modeling techniques for any given application.

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Section: Acoustic Transmission Optionsmentioning

confidence: 99%

Advanced Applications for Underwater Acoustic Modeling

Etter

2012

Advances in Acoustics and Vibration

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“…Appropriately choosing the PDF of V will lead to other standard distributions (e.g., a Rayleigh mixture is obtained when V is a discrete RV). The PDF of X under both a Rayleigh background and Kdistributed clutter (respectively, (14) and (15)) is shown in Fig. 2 for the FT for 0, 5, and 10 dB SNR and a K-distribution shape parameter α = 1, which represents very heavy-tailed clutter.…”

Section: Accounting For Non-gaussian Backgroundsmentioning

confidence: 99%

“…Such approximations are also imperative for performance prediction in tactical decision aids implemented using in situ measurements [13, pg. 30] [14] where computational resources are limited. In this paper, an approach for extending these approximations to account for non-Gaussian backgrounds (equivalently, non-Rayleighdistributed envelopes) is described and evaluated.…”

Section: Introductionmentioning

confidence: 99%

Detection-Threshold Approximation for Non-Gaussian Backgrounds

Abraham

2010

IEEE J. Oceanic Eng.

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Abstract-The detection threshold (DT) term in the sonar equation describes the signal-to-noise ratio (SNR) required to achieve a specified probability of detection (P d ) for a given probability of false alarm (P f a ). Direct evaluation of DT requires obtaining the detector threshold (h) as a function of P f a and then using h while inverting the often complicated relationship between SNR and P d . However, easily evaluated approximations to DT exist when the background additive noise or reverberation is Gaussian (i.e., has a Rayleigh-distributed envelope). While these approximations are extremely accurate for Gaussian backgrounds, they are erroneously low when the background has a heavy-tailed probability density function. In this paper it is shown that by obtaining h appropriately from the non-Gaussian background while approximating P d for a target in the non-Gaussian background by that for a Gaussian background, the easily evaluated approximations to DT extend to non-Gaussian backgrounds with minimal loss in accuracy. Both fluctuating and non-fluctuating targets are considered in Weibulland K-distributed backgrounds. While the P d approximation for fluctuating targets is very accurate, it is coarser for nonfluctuating targets, necessitating a correction factor to the DT approximations.

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