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Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game.

Chenyan Xue1,2, Ling Wang1, Daiyin Zhu1

  • 1Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm for optimizing radar dwell time in Low Probability of Intercept (LPI) networks. The method enhances target tracking efficiency while minimizing overall radar network dwell time for improved LPI performance.

Keywords:
Nash bargaining solution (NBS)cooperative gamedwell time allocationlow probability of intercept (LPI)radar network

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Area of Science:

  • Radar Systems Engineering
  • Game Theory Applications
  • Signal Processing

Background:

  • Managing dwell time is critical for effective multi-target tracking in Low Probability of Intercept (LPI) radar networks.
  • Existing methods face challenges in balancing detection performance with the need for low interception probability.

Purpose of the Study:

  • To propose a cooperative game theory-based algorithm for optimizing radar dwell time allocation in LPI networks.
  • To minimize the total dwell time of the radar network while ensuring target detection performance.
  • To enhance the overall Low Probability of Intercept (LPI) capabilities.

Main Methods:

  • Developed a Nash Bargaining Solution (NBS) algorithm for dwell time allocation.
  • Decomposed the problem into dwell time and target allocation subproblems using dwell time and target allocation indicators.
  • Employed Lagrange relaxation and Newton iteration for dwell time optimization.
  • Utilized the Hungarian algorithm for target allocation.

Main Results:

  • The proposed NBS algorithm effectively optimizes dwell time allocation for each radar.
  • Simulation results demonstrate a significant reduction in the total dwell time of the radar network.
  • The algorithm successfully improves the Low Probability of Intercept (LPI) performance.

Conclusions:

  • The cooperative game theory approach provides an effective solution for dwell time management in LPI radar networks.
  • The proposed algorithm balances detection requirements with LPI constraints, leading to enhanced network efficiency.
  • This method offers a practical strategy for improving radar network performance in complex tracking scenarios.