Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

doi:10.1007/s12204-021-2372-7

J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (5): 649-659.doi: 10.1007/s12204-021-2372-7

收稿日期:2019-09-02 出版日期:2022-09-28 发布日期:2022-09-03

Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

PANG Ce∗ (庞策), SHAN Ganlin (单甘霖)

(Department of Electronic and Optical Engineering, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050003, China)

Received:2019-09-02 Online:2022-09-28 Published:2022-09-03

摘要/Abstract

Abstract: Sensor management schemes are calculated to reduce target threat level assessment risk in this paper. Hidden Markov model and risk theory are combined to build the target threat level model firstly. Then the target threat level estimation risk is defined. And the sensor management schemes are optimized with the smallest target threat level assessment risk. What’s more, the game theory is applied to calculate the optimal sensor management scheme. Some simulations are conducted to prove that the proposed sensor management method is effective.

Key words: target threat level assessment, sensor management, hidden Markov model, risk theory, game theory

中图分类号:

TP219

. [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 649-659.

PANG Ce∗ (庞策), SHAN Ganlin (单甘霖). Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk[J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 649-659.

参考文献 20

[1]	YANG C, KAPLAN L, BLASCH E. Performance measures of covariance and information matrices in resource management for target state estimation [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2594-2613.
[2]	GHASEMIFARD F, NORGREN M. Sensor selection via convex optimization in remote contact-free measurement of currents [C]//2017 International Applied Computational Electromagnetics Society Symposium- Italy (ACES). Florence: IEEE, 2017: 1-2.
[3]	JOSHI S, BOYD S. Sensor selection via convex optimization [J]. IEEE Transactions on Signal Processing, 2009, 57(2): 451-462.
[4]	XU G G, PANG C, DUAN X S, et al. Multi-sensor optimization scheduling for target tracking based on PCRLB and a novel intercept probability factor [J]. Electronics, 2019, 8(2): 140.
[5]	MANYIKA J M, DURRANT-WHYTE H F. Information-theoretic approach to management in decentralized data fusion [C]//Proc. SPIE 1828, Sensor Fusion V, 1992, 1828: 202-213.
[6]	KATSILIERIS F, BOERS Y, DRIESSEN H. Optimal search: A practical interpretation of informationdriven sensor management [C]//2012 15th International Conference on Information Fusion. Singapore: IEEE, 2012: 439-446.
[7]	QIAO C L, SHAN G L, YAN L P, et al. Nonmyopic sensor scheduling for target tracking with emission control [J]. International Journal of Adaptive Control and Signal Processing, 2019, 33(5): 767-783. [8] EDUARDO GOMES BORGES M, MALTESE D, VANHEEGHE P, et al. Sensor management using expected risk reduction approach [C]//2016 19th International Conference on Information Fusion (FUSION). Heidelberg: IEEE, 2016: 2050-2058.
[9]	GOMES-BORGES M E, MALTESE D, VANHEEGHE P, et al. A risk-based sensor management using random finite sets and POMDP [C]//2017 20th International Conference on Information Fusion (Fusion). Xi’an: IEEE, 2017: 1588-1596.
[10]	OZTURK O, BEGEN M A, ZARIC G S. A branch and bound algorithm for scheduling unit size jobs on parallel batching machines to minimize makespan [J]. International Journal of Production Research, 2017, 55(6): 1815-1831.
[11]	JONKER R, VOLGENANT T. Improving the Hungarian assignment algorithm [J]. Operations Research Letters, 1986, 5(4): 171-175.
[12]	LYU Z W, WEI Z C, PAN J, et al. Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm [J]. Applied Soft Computing, 2019, 75: 388-403.
[13]	ZHONG L, HUANG Q Y, WU F, et al. TRADE: A truthful online combinatorial auction for spectrum allocation in cognitive radio networks [J]. Wireless Communications and Mobile Computing, 2015, 15(9): 1320-1330.
[14]	GUPTA A K, GALLASCH G E. Equivalence class verification of the contract net protocol-extension [J]. International Journal on Software Tools for Technology Transfer, 2016, 18(6): 685-706.
[15]	SEVERSON T A, PALEY D A. Distributed multitarget search and track assignment with consensus-based coordination [J]. IEEE Sensors Journal, 2015, 15(2): 864-875.
[16]	KATSILIERIS F, DRIESSEN H, YAROVOY A. Threat-based sensor management for target tracking [J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(4): 2772-2785.
[17]	CHEN H, HE Z L, LIAN F, et al. Threat assessment based sensor control for multi-target tracking [J]. Journal of Electronics & Information Technology, 2018, 40(12): 2861-2867(in Chinese).
[18]	HARE J Z, GUPTA S, WETTERGREN T A. POSE: prediction-based opportunistic sensing for energy efficiency in sensor networks using distributed supervisors [J]. IEEE Transactions on Cybernetics, 2018, 48(7): 2114-2127.
[19]	FINE S, SINGER Y, TISHBY N. The hierarchical hidden Markov model: Analysis and applications [J]. Machine Learning, 1998, 32(1): 41-62.
[20]	SHAFER G. A mathematical theory of evidence [M]. Princeton: Princeton University Press, 1976. [21] LIU H Q, WEI X Z, LI F, et al. The real time control method of radar single radiation power based on RF stealth at the tracking [J]. Acta Electronica Sinica, 2015, 43(10): 2047-2052 (in Chinese). [22] LIU T, DENG Y, CHAN F. Evidential supplier selection based on DEMATEL and game theory [J]. International Journal of Fuzzy Systems, 2018, 20(4): 1321- 1333.

Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk

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