A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

Grasso, Raffaele; Cococcioni, Marco; Mourre, Baptiste; Osler, John C.; Chiggiato, Jacopo

doi:10.1016/j.eswa.2012.12.080

Cited by 16 publications

(4 citation statements)

References 41 publications

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“…In ref. [14], a biobjective GA is used to optimise placement for surveillance applications maximising both the temporal mean of the network area coverage and the tracking coverage.…”

Section: Background and Related Workmentioning

confidence: 99%

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

Taylor

Maskell

Ralph

2022

IET Radar Sonar & Navi

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This paper presents a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. We model the problem as a biobjective one, where the aim is to minimise both sensor placement time and uncertainty over target localisation. Both objectives may be important in time-critical localisation scenarios and our approach allows an operator to choose between different optimal solutions, favouring lower placement time or lower localisation uncertainty as operational circumstances require. We develop a two-phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto-non-dominated solutions to a static problem and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. We find that the evolutionary algorithm improves significantly on standard grid patterns and that the reinforcement learning algorithm improves further on the evolutionary phase. The number of sonobuoys required may also be reduced.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In ref. [14], a biobjective GA is used to optimise placement for surveillance applications maximising both the temporal mean of the network area coverage and the tracking coverage.…”

Section: Background and Related Workmentioning

confidence: 99%

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

Taylor

Maskell

Ralph

2022

IET Radar Sonar & Navi

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“…Many of these mission planning software use Multi-Objective Optimization (MOO) algorithms in order to realistically and satisfactorily achieve observation missions [1][2][3][4][5].…”

Section: Motivationmentioning

confidence: 99%

“…Maritime traffic is part of the most important traffic on Earth, especially involving trade [34]. RMFs are important in the maritime domain given the need for countries to ensure near-permanent surveillance in waters since the number of pirate attacks are still increasing 5 . Like several articles mentioned earlier, [33,35] are based on continuous monitoring of maritime areas to detect suspect events and intervene if needed.…”

Section: Mission Planning In Risk Management Frameworkmentioning

confidence: 99%

Multi-Objective Heterogeneous Multi-Asset Collection Scheduling Optimization with High-Level Information Fusion

Kande

Abielmona

Harb

et al. 2021

2021 IEEE International Systems Conference (SysCon)

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Surveillance of areas of interest through image acquisition is becoming increasingly essential for intelligence services. Several types of platforms equipped with sensors are used to collect good quality images of the areas to be monitored. The evolution of this field has different levels: some studies are only based on improving the quality of the images acquired through sensors, others on the efficiency of platforms such as satellites, aircraft and vessels which

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“…Regarding the assumptions considered in the scope of this study, we first consider that the sensors and targets are stationary (refer to (Grasso et al, 2013) for work on the deployment of sonobuoys with oceanic drift in a monostatic case) and evolve in a two-dimensional space with homogeneous environmental conditions. Additionally, we require that at most one source and at most one receiver may be deployed on a given deployment position (which would then correspond to a post).…”

Section: Introductionmentioning

confidence: 99%

An improved two-phase heuristic for active multistatic sonar network configuration

Thuillier,

Le Josse,

Olteanu

et al. 2024

Expert Systems with Applications

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A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

Cited by 16 publications

References 41 publications

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

Multi-Objective Heterogeneous Multi-Asset Collection Scheduling Optimization with High-Level Information Fusion

An improved two-phase heuristic for active multistatic sonar network configuration

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