IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v17y2021i7p15501477211030121.html
   My bibliography  Save this article

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

Author

Listed:
  • Chenggang Wang
  • Zengfu Wang
  • Xiong Xu
  • Yuhang Hao

Abstract

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.

Suggested Citation

  • Chenggang Wang & Zengfu Wang & Xiong Xu & Yuhang Hao, 2021. "A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.
  • Handle: RePEc:sae:intdis:v:17:y:2021:i:7:p:15501477211030121
    DOI: 10.1177/15501477211030121
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15501477211030121
    Download Restriction: no

    File URL: https://libkey.io/10.1177/15501477211030121?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. Zengfu Wang & Bill Moran & Xuezhi Wang & Quan Pan, 2015. "An accelerated continuous greedy algorithm for maximizing strong submodular functions," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 1107-1124, November.
    2. Zengfu Wang & Bill Moran & Xuezhi Wang & Quan Pan, 2016. "Approximation for maximizing monotone non-decreasing set functions with a greedy method," Journal of Combinatorial Optimization, Springer, vol. 31(1), pages 29-43, January.
    3. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    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. Ruiqi Yang & Dachuan Xu & Longkun Guo & Dongmei Zhang, 2021. "Sequence submodular maximization meets streaming," Journal of Combinatorial Optimization, Springer, vol. 41(1), pages 43-55, January.
    2. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    3. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    4. Guanyi Wang, 2024. "Robust Network Targeting with Multiple Nash Equilibria," Papers 2410.20860, arXiv.org, revised Nov 2024.
    5. Jiaming Hu & Dachuan Xu & Donglei Du & Cuixia Miao, 2024. "Differentially private submodular maximization with a cardinality constraint over the integer lattice," Journal of Combinatorial Optimization, Springer, vol. 47(4), pages 1-24, May.
    6. Rad Niazadeh & Negin Golrezaei & Joshua Wang & Fransisca Susan & Ashwinkumar Badanidiyuru, 2023. "Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization," Management Science, INFORMS, vol. 69(7), pages 3797-3817, July.
    7. Alexandre D. Jesus & Luís Paquete & Arnaud Liefooghe, 2021. "A model of anytime algorithm performance for bi-objective optimization," Journal of Global Optimization, Springer, vol. 79(2), pages 329-350, February.
    8. Bin Liu & Miaomiao Hu, 2022. "Fast algorithms for maximizing monotone nonsubmodular functions," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1655-1670, July.
    9. repec:dgr:rugsom:99a17 is not listed on IDEAS
    10. Lehmann, Daniel, 2020. "Quality of local equilibria in discrete exchange economies," Journal of Mathematical Economics, Elsevier, vol. 88(C), pages 141-152.
    11. Michael Kahr & Markus Leitner & Ivana Ljubić, 2024. "The Impact of Passive Social Media Viewers in Influence Maximization," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1362-1381, December.
    12. Eric DuBois & Ashley Peper & Laura A. Albert, 2023. "Interdicting Attack Plans with Boundedly Rational Players and Multiple Attackers: An Adversarial Risk Analysis Approach," Decision Analysis, INFORMS, vol. 20(3), pages 202-219, September.
    13. Shengminjie Chen & Donglei Du & Wenguo Yang & Suixiang Gao, 2024. "Maximizing stochastic set function under a matroid constraint from decomposition," Journal of Combinatorial Optimization, Springer, vol. 48(1), pages 1-21, August.
    14. Zhenning Zhang & Bin Liu & Yishui Wang & Dachuan Xu & Dongmei Zhang, 2022. "Maximizing a monotone non-submodular function under a knapsack constraint," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1125-1148, July.
    15. Yifan Xiong & Youze Lang & Ziyan Li, 2024. "Cost intervention in delinquent networks," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 62(2), pages 321-344, March.
    16. Zhenning Zhang & Donglei Du & Yanjun Jiang & Chenchen Wu, 2021. "Maximizing DR-submodular+supermodular functions on the integer lattice subject to a cardinality constraint," Journal of Global Optimization, Springer, vol. 80(3), pages 595-616, July.
    17. Awi Federgruen & Nan Yang, 2008. "Selecting a Portfolio of Suppliers Under Demand and Supply Risks," Operations Research, INFORMS, vol. 56(4), pages 916-936, August.
    18. Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
    19. Yanzhi Li & Zhicheng Liu & Chuchu Xu & Ping Li & Xiaoyan Zhang & Hong Chang, 2023. "Two-stage submodular maximization under curvature," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-16, March.
    20. Simon Bruggmann & Rico Zenklusen, 2019. "Submodular Maximization Through the Lens of Linear Programming," Management Science, INFORMS, vol. 44(4), pages 1221-1244, November.
    21. Xiaojuan Zhang & Qian Liu & Min Li & Yang Zhou, 2022. "Fast algorithms for supermodular and non-supermodular minimization via bi-criteria strategy," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3549-3574, December.

    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:sae:intdis:v:17:y:2021:i:7:p:15501477211030121. 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: SAGE Publications (email available below). General contact details of provider: .

    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.