IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v60y2012i2p292-312.html
   My bibliography  Save this article

Stochastic Optimization of Sensor Placement for Diver Detection

Author

Listed:
  • Anton Molyboha

    (Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey 07030)

  • Michael Zabarankin

    (Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, New Jersey 07030)

Abstract

A comprehensive framework for diver detection by a hydrophone network in an urban harbor is presented. It includes a signal processing algorithm and a diver detection test and formulates optimal hydrophone placement as a two-stage stochastic optimization problem with respect to different scenarios of underwater noise. The signal processing algorithm identifies sound intensity peaks associated with diver breathing and outputs a diver number measuring the likelihood of diver presence, whereas the diver detection test aggregates the diver numbers obtained from the hydrophones in a linear statistic and optimizes the statistic's coefficients and a detection threshold for each noise scenario. The serial dependence of the diver numbers on a short time scale (several detection periods) is modeled by a hidden Markov chain, and finding the worst-case diver's trajectory for each hydrophone placement and noise scenario is reduced to a linear programming problem. The framework is tested in numerical experiments with real-life data for circular and elliptic hydrophone placements and is shown to be superior to a deterministic energy-based approach.

Suggested Citation

  • Anton Molyboha & Michael Zabarankin, 2012. "Stochastic Optimization of Sensor Placement for Diver Detection," Operations Research, INFORMS, vol. 60(2), pages 292-312, April.
  • Handle: RePEc:inm:oropre:v:60:y:2012:i:2:p:292-312
    DOI: 10.1287/opre.1110.1032
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1110.1032
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.1110.1032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
    2. Sergei Pashko & Anton Molyboha & Michael Zabarankin & Sergei Gorovyy, 2008. "Optimal sensor placement for underwater threat detection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(7), pages 684-699, October.
    3. G. Hanoch & H. Levy, 1969. "The Efficiency Analysis of Choices Involving Risk," Review of Economic Studies, Oxford University Press, vol. 36(3), pages 335-346.
    4. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, December.
    5. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang Lu & John J. Hasenbein & David P. Morton, 2016. "Modeling and Optimization of a Spatial Detection System," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 512-526, August.
    2. Oleg Burdakov & Jonas Kvarnström & Patrick Doherty, 2017. "Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles," Annals of Operations Research, Springer, vol. 249(1), pages 163-174, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wong, Wing-Keung & Phoon, Kok Fai & Lean, Hooi Hooi, 2008. "Stochastic dominance analysis of Asian hedge funds," Pacific-Basin Finance Journal, Elsevier, vol. 16(3), pages 204-223, June.
    2. Leili Javanmardi & Yuri Lawryshyn, 2016. "A new rank dependent utility approach to model risk averse preferences in portfolio optimization," Annals of Operations Research, Springer, vol. 237(1), pages 161-176, February.
    3. Kallio, Markku & Dehghan Hardoroudi, Nasim, 2018. "Second-order stochastic dominance constrained portfolio optimization: Theory and computational tests," European Journal of Operational Research, Elsevier, vol. 264(2), pages 675-685.
    4. Martin Branda & Miloš Kopa, 2014. "On relations between DEA-risk models and stochastic dominance efficiency tests," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(1), pages 13-35, March.
    5. Adam, Lukáš & Branda, Martin, 2021. "Risk-aversion in data envelopment analysis models with diversification," Omega, Elsevier, vol. 102(C).
    6. Miloš Kopa & Vittorio Moriggia & Sebastiano Vitali, 2018. "Individual optimal pension allocation under stochastic dominance constraints," Annals of Operations Research, Springer, vol. 260(1), pages 255-291, January.
    7. Andrey Lizyayev, 2012. "Stochastic dominance efficiency analysis of diversified portfolios: classification, comparison and refinements," Annals of Operations Research, Springer, vol. 196(1), pages 391-410, July.
    8. Leili Javanmardi & Yuri Lawryshyn, 2016. "A new rank dependent utility approach to model risk averse preferences in portfolio optimization," Annals of Operations Research, Springer, vol. 237(1), pages 161-176, February.
    9. Gómez, Fabio & Tang, Qihe & Tong, Zhiwei, 2022. "The gradient allocation principle based on the higher moment risk measure," Journal of Banking & Finance, Elsevier, vol. 143(C).
    10. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Wang, 2002. "Consistent testing for stochastic dominance: a subsampling approach," CeMMAP working papers 03/02, Institute for Fiscal Studies.
    11. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    12. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    13. Battigalli, Pierpaolo & Bonanno, Giacomo, 1997. "The Logic of Belief Persistence," Economics and Philosophy, Cambridge University Press, vol. 13(1), pages 39-59, April.
    14. Kwame Addae‐Dapaah & Wilfred Tan Yong Hwee, 2009. "The unsung impact of currency risk on the performance of international real property investment," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 56-65, January.
    15. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    16. Szabó, György & Borsos, István & Szombati, Edit, 2019. "Games, graphs and Kirchhoff laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 416-423.
    17. Giovanni Masala & Filippo Petroni, 2023. "Drawdown risk measures for asset portfolios with high frequency data," Annals of Finance, Springer, vol. 19(2), pages 265-289, June.
    18. Ke Zhou & Jiangjun Gao & Duan Li & Xiangyu Cui, 2017. "Dynamic mean–VaR portfolio selection in continuous time," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1631-1643, October.
    19. Chambers, Marcus J & Bailey, Roy E, 1996. "A Theory of Commodity Price Fluctuations," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 924-957, October.
    20. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:60:y:2012:i:2:p:292-312. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.