Information Fusion

Multi-sensor management based on joint risk prediction under aerial electronic jamming

  • Guangxin ZHANG ,
  • Lin ZHOU ,
  • Zheng ZHAO ,
  • Qian WEI ,
  • Jiayuan YAN
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  • School of Artificial Intelligence,Henan University,Zhengzhou 450046,China

Received date: 2025-10-14

  Revised date: 2025-10-26

  Accepted date: 2025-11-04

  Online published: 2025-11-13

Supported by

National Natural Science Foundation of China(62303162);Key R&D Project of Henan Provincial Department of Science and Technology(31111212500);Science and Technology Development Plan of Henan Provincial Department of Science and Technology(252102211113)

Abstract

Traditional multi-sensor management methods based on risk assessment face difficulties such as difficulty in risk evaluation and poor tracking accuracy under electromagnetic interference scenarios, making it challenging to effectively ensure the overall safety and accuracy of target tracking systems. To address this problem, this paper comprehensively considers the sensor radiation risk, detection loss risk, and target threat risk in electronic interference scenarios, and proposes a multi-sensor management method based on bidirectional joint risk multi-step prediction under electronic interference. First, by taking into account the radiation risk of the sensor side, the detection loss risk, and the target threat risk from the enemy, and by introducing adaptive weights based on the Signal to Interference plus Noise Ratio (SINR),a variable-weighted bidirectional joint risk model is constructed. Then, with the objective of minimizing sensor power, a multi-step prediction multi-sensor allocation problem based on bidirectional joint risk is formulated within a time-series prediction framework. Finally, the multi-sensor allocation problem with non-convex constraints is relaxed into a convex optimization problem for efficient solving, thereby improving computational efficiency. Simulation results show that the proposed method can effectively schedule and allocate limited multi-sensor resources, ensuring the safety of the tracking system while effectively improving the accuracy of target tracking under electronic interference scenarios.

Cite this article

Guangxin ZHANG , Lin ZHOU , Zheng ZHAO , Qian WEI , Jiayuan YAN . Multi-sensor management based on joint risk prediction under aerial electronic jamming[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(S1) : 732906 -732906 . DOI: 10.7527/S1000-6893.2025.32906

References

[1] XU C, ZHAO W Z, WANG C Y. An integrated threat assessment algorithm for decision-making of autonomous driving vehicles[J]. IEEE Transactions on Intelligent Transportation Systems202021(6): 2510-2521.
[2] 刘祥雨, 王刚, 王思远, 等. 数据-知识双驱动的编队目标意图识别方法[J]. 航空学报202647(2): 288-308.
  LIU X Y, WANG G, WANG S Y, et al. A data-knowledge dual-driven method for formation target intention recognition[J]. Acta Aeronautica et Astronautica Sinica202647(2): 288-308 (in Chinese).
[3] CHAWLA A, SINGH R K, PATEL A, et al. Distributed detection for centralized and decentralized millimeter wave massive MIMO sensor networks[J]. IEEE Transactions on Vehicular Technology202170(8): 7665-7680.
[4] LI X T, ZHANG T X, YI W, et al. Radar selection based on the measurement information and the measurement compensation for target tracking in radar network[J]. IEEE Sensors Journal201919(18): 7923-7935.
[5] LI Z J, WEI Y J, XIE J W, et al. Resource-saving scheduling scheme for centralized target tracking in multiple radar system under automatic blanket jamming[J]. Chinese Journal of Aeronautics202437(5): 349-362.
[6] YANG Q W, JIANG L B, ZHENG S Y, et al. Joint multi-dimensional resource scheduling for cooperative tracking of multiple LEO targets via space-based radar networks[J]. Chinese Journal of Aeronautics202639(4): 103705.
[7] XIE M C, YI W, KIRUBARAJAN T, et al. Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks[J]. IEEE Transactions on Signal Processing201866(3): 729-743.
[8] BOSTR?M-ROST P, AXEHILL D, HENDEBY G. Sensor management for search and track using the Poisson multi-bernoulli mixture filter[J]. IEEE Transactions on Aerospace and Electronic Systems202157(5): 2771-2783.
[9] YANG Y G, LIAO L F, YANG H, et al. An optimal control strategy for multi-UAVs target tracking and cooperative competition[J]. IEEE/CAA Journal of Automatica Sinica20218(12): 1931-1947.
[10] 田晨, 裴扬, 侯鹏, 等. 基于决策不确定性的多目标跟踪传感器管理[J]. 航空学报202041(10): 323781.
  TIAN C, PEI Y, HOU P, et al. Decision uncertainty based sensor management for multi-target tracking[J]. Acta Aeronautica et Astronautica Sinica202041(10): 323781 (in Chinese).
[11] 张昀普, 单甘霖. 面向空中目标威胁评估的多传感器管理方法[J]. 航空学报201940(11): 240-253.
  ZHANG Y P, SHAN G L. Multi-sensor management approach for aerial target threat assessment[J]. Acta Aeronautica et Astronautica Sinica201940(11): 240-253 (in Chinese).
[12] ZHANG Z N, SHAN G L. UTS-based foresight optimization of sensor scheduling for low interception risk tracking[J]. International Journal of Adaptive Control and Signal Processing201428(10): 921-931.
[13] DONG Q, PANG C. Risk-based non-myopic sensor scheduling in target threat level assessment[J]. IEEE Access20219: 76379-76394.
[14] MENG F Q, TIAN K S, WU C F. Deep reinforcement learning-based radar network target assignment[J]. IEEE Sensors Journal202121(14): 16315-16327.
[15] SHAN G L, PANG C. Distributed sensor management based on target losing probability for maneuvering multi-target tracking[J]. IEEE Access20208: 113610-113623.
[16] ZHOU L, WU J W, WEI Q, et al. Multi-sensor scheduling method based on joint risk assessment with variable weight[J]. Entropy202224(9): 1315.
[17] PANG C, SHAN G L. Sensor scheduling based on risk for target tracking[J]. IEEE Sensors Journal201919(18): 8224-8232.
[18] SHAN G L, XU G G, QIAO C L. A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control[J]. Defence Technology202016(1): 242-250.
[19] NAGATA T, YAMADA K, NAKAI K, et al. Randomized group-greedy method for large-scale sensor selection problems[J]. IEEE Sensors Journal202323(9): 9536-9548.
[20] JOSHI S, BOYD S. Sensor selection via convex optimization[J]. IEEE Transactions on Signal Processing200957(2): 451-462.
[21] RUSU C, THOMPSON J, ROBERTSON N M. Sensor scheduling with time, energy, and communication constraints[J]. IEEE Transactions on Signal Processing201866(2): 528-539.
[22] JIN Y, ZHOU L, ZHANG L, et al. A novel range-free node localization method for wireless sensor networks[J]. IEEE Wireless Communications Letters202211(4): 688-692.
[23] LIU L, JI H B, ZHANG W B, et al. Multi-sensor multi-target tracking using probability hypothesis density filter[J]. IEEE Access20197: 67745-67760.
[24] SINGH C, JIRUTITIJAROEN P, MITRA J. Electric power grid reliability evaluation[M]. Hoboken: Wiley, 2018: 57-61.
[25] ZUO L, HU J, SUN H, et al. Resource allocation for target tracking in multiple radar architectures over lossy networks[J]. Signal Processing2023208:108973.
[26] PANG C, XU G G, SHAN G L, et al. A new energy efficient management approach for wireless sensor networks in target tracking[J]. Defence Technology202117(3): 932-947.
[27] 刘先省, 申石磊, 潘泉. 传感器管理及方法综述[J]. 电子学报200230(3): 394-398.
  LIU X S, SHEN S L, PAN Q. A survey of sensor management and methods[J]. Acta Electronica Sinica200230(3): 394-398 (in Chinese).
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