Analysis of Passive Sonobuoy Array Optimal Placement Based on Genetic Algorithm

Zhou, Xu; Yang, Ri-Jie; Gao, Xiaofeng; Han, Jianhui

doi:10.3724/sp.j.1146.2007.00546

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In ref. [13], optimal placement of passive sonobuoys is approached by using a GA which is shown to be capable of achieving a significant improvement in detection probability compared to standard methods. In ref.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Compared with Von Neumann corrector [8] and xor chain method [9] , in efficiency, the bit rates is twice of the first method, and is n/2 times of the latter method. In performance, the method presented in this paper can improve the randomicity and indeterminism of the output stream due to the combination of the xor of adjacent bits and interval bits.…”

Section: Fig 3 Drive and Reset Circuitmentioning

confidence: 99%

An Improved Random Number Generator Based on Chaotic Map

Zheng

2012

AMM

View full text Add to dashboard Cite

This paper pointed out that we can determine the initialization x0 when the random sequence is generated by the piecewise linear chaotic map in finite precision, and aimed at solving this objection, an improved measure of perturbing the system variable timely is proposed, and an improved random number generator based on chaotic map is designed and realized. Meanwhile, their working process and theory are analyzed, and the simulation, validation and test of the whole system is finished, and the result indicated that the bit rates is up to 20 MHz and the random number could pass the FIPS140-2 randomness test.

show abstract

Analysis of Passive Sonobuoy Array Optimal Placement Based on Genetic Algorithm

Cited by 2 publications

References 2 publications

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

An Improved Random Number Generator Based on Chaotic Map

Contact Info

Product

Resources

About