Research on Power Allocation Method for Networked Radar Based on Extended Game Theory

YE Fang; QI Changlong; SUN Liuqing; LI Yibing

doi:10.11999/JEIT241131

Volume 47 Issue 6

Jun. 2025

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YE Fang, QI Changlong, SUN Liuqing, LI Yibing. Research on Power Allocation Method for Networked Radar Based on Extended Game Theory[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1803-1815. doi: 10.11999/JEIT241131

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YE Fang, QI Changlong, SUN Liuqing, LI Yibing. Research on Power Allocation Method for Networked Radar Based on Extended Game Theory[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1803-1815. doi: 10.11999/JEIT241131

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Research on Power Allocation Method for Networked Radar Based on Extended Game Theory

doi: 10.11999/JEIT241131 cstr: 32379.14.JEIT241131

1.
Institute of Information and Communication Engineering, Harbin Engineering University, Harbin 150000, China
2.
Key Laboratory of Advanced Ship Communication and Information Technology, Harbin Engineering University, Harbin 150000, China

Funds: Heilongjiang Higher Education Teaching Reform Project (SJGY20210233)

Received Date: 2024-12-25
Rev Recd Date: 2025-05-17

Available Online: 2025-06-09

Publish Date: 2025-06-30

Abstract

Abstract

Objective As jamming technology grows increasingly sophisticated, networked radar systems in penetration countermeasure scenarios often operate under partial information, which markedly reduces detection performance. Strategic power allocation can improve spatial and frequency diversity, thereby enhancing target detection. However, most existing methods optimize radar resource distribution in isolation, without accounting for the dynamic interactions between radars and jammers. To address this limitation, this paper proposes a power allocation method for networked radar based on extensive-form game theory. The allocation problem is modeled under partial observability, and the Deep CounterFactual Regret minimization (Deep CFR) algorithm is employed to solve it. This approach increases the probability of successfully detecting penetration targets in adversarial environments. Methods A power allocation model for networked radar is developed in parallel with an information-loss model that captures the adversarial dynamics between networked radar and jammer swarms. Drawing on the principles of extensive-form games, the fundamental elements are defined and used to construct an extensive-form game model for radar power allocation. In this framework, networked radar aggregates observable information to mitigate the effects of unobservable jammer signals. To solve the game, the Deep CFR algorithm is employed, integrating deep learning with regret minimization to approximate Nash equilibrium strategies. This approach addresses the storage and computational challenges associated with traditional extensive-form game solutions. Simulation results confirm that the proposed method allocates radar power effectively under partial observation, improving the probability of target detection. Results and Discussions Simulation results show that under partial observation conditions, the proposed method achieves a detection probability of 0.813, exceeding the performance of random strategies, Deep Deterministic Policy Gradient (DDPG), and Double Deep Q-Network (Double DQN). While ensuring stable convergence, the method also reduces training time by 27.8% and 31.5% compared with DDPG and Double DQN, respectively. Sensitivity analysis indicates that detection performance declines with an increasing number of jammers due to stronger interference. Additionally, variations in the number of missing information elements (M) demonstrate that overall radar performance depends on both the extent of information loss and the intensity of coordinated jamming. When jamming degradation outweighs the benefits of reduced information loss, the detection probability decreases accordingly. Conclusions In modern electronic warfare, where jammers employ complex and adaptive interference strategies and networked radar systems operate with partial adversary information, this study proposes an effective approach to power resource management. By modeling the dynamic interaction between radar systems and jammer swarms through extensive-form game theory and applying the Deep CFR algorithm, simulation results demonstrate the following: (1) The near-Nash equilibrium strategy aligns with the optimal allocationobtained using the Sparrow Search Algorithm, confirming its validity; (2) The proposed method achieves higher detection probability (0.813) than random strategies, DDPG, and Double DQN; and (3) It reduces training time significantly compared with DDPG and Double DQN. Future work will extend this approach to other resource management dimensions, including waveform selection and beam dwell time optimization.
- Netted radar system,
- Extensive form game,
- Power allocation

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References(36)

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