A GPS sonobuoy localization system

Whitehouse, H.J.; Alsup, J.M.; Escobar, Anna Leese de; Sullivan, S.F.

doi:10.1109/plans.2004.1309024

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…Localization is a critical and challenging responsibility for UWSNs. Because of the differences in underwater environmental circumstances, the localization algorithms proposed in References37–43 are inappropriate for UWASNs because they are based on GPS for terrestrial sensor networks.…”

Section: Localization Strategies In Underwater Acoustic Sensor Networkmentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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Section: Localization Strategies In Underwater Acoustic Sensor Networkmentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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“…Previously, the operation depended on a monitoring aircraft to perform a mark on top (MOT), which is time consuming, provides insufficient accuracy, increases the vulnerability of the aircraft, and generally provides a tactical rather than a geographic position for the target. Sonobuoys equipped with GPS for positioning are known as GPS sonobuoys [7].…”

Section: Gps Sonobuoysmentioning

confidence: 99%

Performance evaluation of a non-linear error model for underwater range computation utilizing GPS sonobuoys

El-Shafie

Osman

Noureldin

et al. 2010

Neural Comput & Applic

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Deployed from an airborne platform or a surface vessel, arrays of GPS sonobuoys can be used to efficiently track and localize submarines. The range of the target of interest can be monitored with the deployed sonobuoys. However, the accuracy deteriorates when the target is on the detection range of only one sonobuoy. The objective of this research is to improve the range computation of the target of interest by establishing a non-linear error model for range error using adaptive neuro-fuzzy inference systems (ANFIS), which has the capabilities of dealing with data of high level of uncertainty and the advantage of being based on neural computation. Furthermore, the performance of the proposed model is examined with both experimental real field data and contact-level simulation data considering different scenarios for both the array of GPS sonobuoys and the target. The results discuss merits and the limitations of the proposed method.

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“…Previously, the operation depended on a monitoring aircraft to perform a Mark On Top (MOT), which is time consuming, provides insufficient accuracy, increases the vulnerability of the aircraft and generally provides a tactical rather than a geographic position for the target. Sonobuoys equipped with GPS for positioning are known as GPS sonobuoys [3] .…”

Section: Gps Sonobuoysmentioning

confidence: 99%

“…The fuzzy Norm operator (Π) (minimum or product) is operated at the second layer of ANFIS and a normalized firing strength i W is computed in the third layer for each (i) consequent [5,6] . The system output (i.e., (δR 1s ) is computed on the basis of TSK fuzzy system as follows: if x is A 1 and y is B 1 , then f 1 = p 1 x + q 1 y + r 1 (3) if x is A 2 and y is B 2 , then f 2 = p 2 x + q 2 y + r 2 (4)…”

Section: Objectivesmentioning

confidence: 99%

Performance Enhancement of Underwater Target Tracking by Fusing Data of Array of Global Positioning System Sonobuoys

El-Shafie

Osman

Noureldin

et al. 2009

Journal of Computer Science

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Problem statement: An accurate knowledge of geographic positions of sonobuoys is critical for the conduct of antisubmarine warfare operations and detected target localization. Deployed from an airborne platform or a surface vessel, arrays of sonobuoys could be used to efficiently track and localize submarines. Lastly, some sonobuoys were being equipped with GPS for improving system accuracy and potentially allowing networked Sonobuoy positioning. However, the computation of the range using the propagation loss profile and the data of one sonobuoy usually leads to inaccurate target localization due to several effects and uncertainties. It was, alternatively, reported that if the target is within the detection rage of two or more sonobuoys, greatly improved target localization can be achieved. Approach: Aim of this research was to investigate the feasibility of fusing data from a distributed field of GPS sonobuoys to create an Artificial Intelligence (AI) based model for the error of the range computation in case of the target being detected by only one sonobuoy. Proposed module was designed utilizing Adaptive Neuron-Fuzzy Inference Systems (ANFIS) to estimate the range error associated with the computation using the propagation loss profile when the target is within the detection range of only one sonobuoy. The architecture of the proposed ANFIS system had two unique features. First was the real-time cross-validation applied during the update (training) procedure of the ANFIS-based module while the target was detected by two sonobuoys and the range was computed. Second feature was the use of non-overlapping and moving window for the real-time implementation of the ANFIS-based data fusion module. Results: Performance of the proposed system was examined with simulation data considering different scenarios for both the array of GPS sonobuoys and the target. Results showed that the corrected positioning by one sonobuoy is completely following the positioning by two sonobuoys over the entire experiment with the error in between evaluated to have RMSE value of 0.004 Nm and 0.008 for both scenarios. Conclusion: These results revealed that with aided from the proposed ANFIS model; significant enhancements to the underwater target tracking accuracy in cases of single sonobuoy detection could be achieved and thus maintaining consistent levels of accuracy over the whole tracking mission.

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A GPS sonobuoy localization system

Cited by 6 publications

References 1 publication

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Performance evaluation of a non-linear error model for underwater range computation utilizing GPS sonobuoys

Performance Enhancement of Underwater Target Tracking by Fusing Data of Array of Global Positioning System Sonobuoys

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