Evidence map›Paper›PMID 40293108›Full record

ArticleSensors (Basel, Switzerland)2025

Enhancing Maritime Domain Awareness Through AI-Enabled Acoustic Buoys for Real-Time Detection and Tracking of Fast-Moving Vessels.

Jeremy Karst, Robert McGurrin, Kimberly Gavin, Joseph Luttrell, William Rippy, Robert Coniglione, Jason McKenna, Ralf Riedel

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jeremy KarstRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.
Robert McGurrinBLUEiQ, 10 Fan Pier Boulevard, Boston, MA 02210, USA.
Kimberly GavinBLUEiQ, 10 Fan Pier Boulevard, Boston, MA 02210, USA.
Joseph LuttrellRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.ORCID 0000-0001-5832-4487
William RippyRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.
Robert ConiglioneRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.
Jason McKennaRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.ORCID 0000-0002-4983-7640
Ralf RiedelRoger F. Wicker Center for Ocean Enterprise, The University of Southern Mississippi, Gulfport, MS 39501, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acoustic target recognition has always played a central role in marine sensing. Traditional signal processing techniques that have been used for target recognition have shown limitations in accuracy, particularly with commodity hardware. To address such limitations, we present the results of our experiments to assess the capabilities of AI-enabled acoustic buoys using OpenEar™, a commercial, off-the-shelf, software-defined hydrophone sensor, for detecting and tracking fast-moving vessels. We used a triangular sparse sensor network to investigate techniques necessary to estimate the detection, classification, localization, and tracking of boats transiting through the network. Emphasis was placed on evaluating the sensor's operational detection range and feasibility of onboard AI for cloud-based data fusion. Results indicated effectiveness for enhancing maritime domain awareness and gaining insight into illegal, unreported, and unregulated activities. Additionally, this study provides a framework for scaling autonomous sensor networks to support persistent maritime surveillance.

Indexed as

acoustic target recognitionartificial intelligencelocalizationmarine domain awareness (MDA)OpenEar™

Identifiers

PMID40293108
PMCPMC11945802

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.