Broadband low frequency sonar for non-imaging based identification

Houston, Brian H.; Bucaro, J. A.; Yoder, Timothy J.; Kraus, Larry A.; Tressler, James F.; Fernandez, Jose E.; Montgomery, Thomas C.; Howarth, Thomaś R.

doi:10.1109/oceans.2002.1193302

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…In contrast, resonance scattering (also referred to as structural acoustics) sonar applications are currently developed to help detect and characterize proud and partially buried objects (Bucaro et al, 2007a(Bucaro et al, , 2007bHouston et al, 2002, Houston et al, 2009. The frequency range is lower than in the aforementioned application with wavelengths on the same order or somewhat longer than the dimensions of the object (Figure 1b).…”

Section: Discussionmentioning

confidence: 99%

Detection and Classification of Buried UXO and Determination of Seafloor Parameters in Littoral Environments using Resonance Scattering Sonar

Gritto¹,

Korneev²,

Johnson³

2010

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Section: Discussionmentioning

confidence: 99%

Detection and Classification of Buried UXO and Determination of Seafloor Parameters in Littoral Environments using Resonance Scattering Sonar

Gritto¹,

Korneev²,

Johnson³

2010

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“…Finally, we select the path that maximizes the approximate characterization gain in (37) subject to the budget constraint in (29).η…”

Section: Approximating the Characterization Gain Of A Pathmentioning

confidence: 99%

“…Our alternative approach assumes that the search paths are composed of sequences of parallel straight lines. This assumption arises often in subsea applications that rely on side-scan imaging sonar (see, for example, [37], [38]). Unlike the proposed approach in Section V-D, our approach in this section can only apply to certain classes of mapping problems.…”

Section: E Approximating the Characterization Gain Of A Cellmentioning

confidence: 99%

Acquiring environmental information yields better anticipated search performance

Yetkin

Lutz

Stilwell

2016

OCEANS 2016 MTS/IEEE Monterey

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We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. We specifically consider applications where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution in this study is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications.

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“…The second area involves the development of suitable signal processing techniques including both tracking and identification algorithms that can operate effectively on the environmentally corrupted threat target signals. These include, among others, those based on kernel matching pursuits [1], relevance vector machines [2][3][4][5], and time reversal mirrors recently developed at NRL [6,7]. The studies include the full range of broadband frequencies.…”

Section: Approachmentioning

confidence: 99%

Harbor Threat Detection, Classification, and Identification

Houston¹,

Carin²

2007

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There is a critical need for reliably and rapidly detecting, identifying, and tracking submerged low observable targets in port environments, which would allow for rapid and effective neutralization of such threats. Without this capability, personnel, naval platforms and targets of opportunity are exposed to a cheap kill by an opportunistic threat. The goal of this effort is to exploit for the first time detailed active and passive acoustic signature information associated with harbor threats together with advanced Bayesian classifier techniques. In this effort the intent is to leverage the highly successful science and technology carried out in the broadband mine identification program [Ref.

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Broadband low frequency sonar for non-imaging based identification

Cited by 15 publications

References 20 publications

Detection and Classification of Buried UXO and Determination of Seafloor Parameters in Littoral Environments using Resonance Scattering Sonar

Detection and Classification of Buried UXO and Determination of Seafloor Parameters in Littoral Environments using Resonance Scattering Sonar

Acquiring environmental information yields better anticipated search performance

Harbor Threat Detection, Classification, and Identification

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