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Article Navigation >  Journal of Radars  > 2024  >  13(1): 270-282
Wu Sunyong, Xue Qiutiao, Zhu Shengqi, Yan Qingzhu, Sun Xiyan. Track-Before-Detect Algorithm for Weak Extended Target Based on Particle Filter under Clutter Environment[J]. Journal of Radars, 2017, 6(3): 252-258. doi: 10.12000/JR16128
Citation: WANG Mingyang, LIU Xuxu, LI Yulin, et al. Dynamic adversarial risk estimation based on labeled multi-Bernoulli tracker[J]. Journal of Radars, 2024, 13(1): 270–282. doi: 10.12000/JR23207
shu

Dynamic Adversarial Risk Estimation Based on Labeled Multi-Bernoulli Tracker

DOI: 10.12000/JR23207
  • 1.

    Southwest Institute of Electronic Technology, Chengdu 610036, China

  • 2.

    School of Micro-electronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds:  The National Natural Science Foundation of China (62301091, 62371078), China Postdoctoral Science Foundation Funded Project (2022M710533, 2022M710535)
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  • Corresponding author: LI Suqi, lisuqi@cqu.edu.cn
  • Received Date: 2023-10-25
  • Rev Recd Date: 2023-12-25
    • Available Online: 2023-12-27
  • Publish Date: 2024-01-09
    Abstract
  • In many military and civilian areas, there exists a scenario in which multiple intruders from an adversary attempt to enter important region of our own to carry out intentional malign activity. Adversarial Risk (AR) estimation is used to assess and predict the expected damage to our valuable assets from the actions of online adversaries based on measurements performed by sensors. To evaluate random and time-varying AR, this study proposes a stochastic AR estimation approach based on a Labeled Multi-Bernoulli (LMB) tracker. First, in the formulation of LMB filtering, expressions of the minimum mean squared error estimation of the stochastic AR are derived for the additive and multiplying model. Second, by combining the Gaussian mixture and sampling approximations, we devise a numerical calculation approach for the proposed AR estimations. Third, we achieve an online evaluation of the expected damage to our valuable assets from the adversary by embedding the proposed AR estimation and LMB filtering. The effectiveness and performance advantage of the proposed estimation algorithms are verified using measurements from radars, considering a simulated scenario wherein multiple lethal targets hit the radar positions.

     

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