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LI Haoqing, YU Dian, PAN Changchun, et al. Multiradar collaborative task scheduling algorithm based on graph neural networks with model knowledge[J]. Journal of Radars, in press. doi: 10.12000/JR24222
Citation: LI Haoqing, YU Dian, PAN Changchun, et al. Multiradar collaborative task scheduling algorithm based on graph neural networks with model knowledge[J]. Journal of Radars, in press. doi: 10.12000/JR24222
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge

DOI: 10.12000/JR24222
  • 1.

    Shanghai Key Laboratory of Navigation and Location based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

  • 2.

    Paris Elite Institute of Technology , Shanghai Jiao Tong University, Shanghai, 200240, China

Funds:  Funding from the Shanghai Municipal Science and Technology Commission (24Z511005506)
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    Abstract
  • Modern radar systems face increasingly complex challenges in tasks such as detection, tracking, and identification. The diversity of task types, limited data resources, and strict execution time requirements make radar task scheduling a strongly NP-hard problem. However, existing scheduling algorithms struggle to efficiently handle multiradar collaborative tasks involving complex logical constraints. Therefore, Artificial Intelligence (AI)-based scheduling algorithms have gained significant attention. However, their efficiency is heavily dependent on effectively extracting the key features of the problem. The ability to quickly and comprehensively extract common features of multiradar scheduling problems is essential for improving the efficiency of such AI scheduling algorithms. Therefore, this paper proposes a Model Knowledge Enhanced Graph Neural Network (MKEGNN) scheduling algorithm. This method frames the radar task collaborative scheduling problem as a heterogeneous network graph, leveraging model knowledge to optimize the training process of the Graph Neural Network (GNN) algorithm. A key innovation of this algorithm is its capability to capture critical model knowledge using low-complexity calculations, which helps to further optimize the GNN model. During the feature extraction stage, the algorithm employs a random unitary matrix transformation. This approach utilizes the spectral features of the random Laplacian matrix from the task’s heterogeneous graph as global features, enhancing the GNN’s ability to extract shared problem features while downplaying individual characteristics. In the parameterized decision-making stage, the algorithm leverages the upper and lower bound knowledge derived from guiding and empirical solutions of the problem model. This strategy significantly reduces the decision space, enabling the network to optimize quickly and accelerating the learning process. Extensive simulation experiments confirm the effectiveness of the MKEGNN algorithm. Compared to existing approaches, it demonstrates improved stability and accuracy across all task sets, boosting the scheduling success rate by 3%～10% and the weighted success rate by 5%～15%. For particularly challenging task sets involving complex multiradar collaborations, the success rate improves by over 4%. The results highlight the algorithm’s stability and robustness.

     

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