Heterogeneous Task Cooperative Scheduling Architecture for Networked Radar in Saturation Attack Air Defense Early Warning

YE Juhang; FANG Yuyuan; WEI Shaopeng; DUAN Jia; ZHANG Lei

doi:10.11999/JEIT260373

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YE Juhang, FANG Yuyuan, WEI Shaopeng, DUAN Jia, ZHANG Lei. Heterogeneous Task Cooperative Scheduling Architecture for Networked Radar in Saturation Attack Air Defense Early Warning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260373

Citation:

YE Juhang, FANG Yuyuan, WEI Shaopeng, DUAN Jia, ZHANG Lei. Heterogeneous Task Cooperative Scheduling Architecture for Networked Radar in Saturation Attack Air Defense Early Warning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260373

Citation:

YE Juhang, FANG Yuyuan, WEI Shaopeng, DUAN Jia, ZHANG Lei. Heterogeneous Task Cooperative Scheduling Architecture for Networked Radar in Saturation Attack Air Defense Early Warning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260373

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Heterogeneous Task Cooperative Scheduling Architecture for Networked Radar in Saturation Attack Air Defense Early Warning

doi: 10.11999/JEIT260373 cstr: 32379.14.JEIT260373

1.
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China
2.
School of Information and Technology, Southwest Jiaotong University, Chengtu 611756, China
3.
School of Oceanography and Information, China University of Petroleum, Qingdao 266580, China

Funds: The National Natural Science Foundation of China (62301612), Shandong Provincial Science and Technology Major Project Support for Young Talents (SDAST2025QTA099), Shandong Provincial Natural Science Foundation (ZR2024MF096), Shandong Provincial Natural Science Foundation (ZR2023QF004), Guangdong Provincial Natural Science Foundation (2025A1515010242)

Received Date: 2026-03-31
Accepted Date: 2026-06-24
Rev Recd Date: 2026-06-21

Available Online: 2026-07-04

Abstract

Abstract

Objective To address the severe challenges posed by Unmanned Aerial Vehicle (UAV) swarms and intelligent loitering munitions, which generate massive, sudden, and heterogeneous early warning tasks during saturation attacks, a scalable networked-radar cooperative scheduling architecture is proposed. Existing architectures suffer from rigid dynamic coordination and insufficient capability for heterogeneous task scheduling. The non-convex cooperative scheduling problem is therefore decoupled into a multi-stage decision process consisting of multidimensional dynamic resource coordination and adaptive heterogeneous task scheduling. By incorporating a dispatch mechanism and Hierarchical Reinforcement Learning (HRL), a hybrid architecture integrating network-level centralized dynamic target allocation with node-level distributed heterogeneous task scheduling is developed. The architecture is implemented through an execution-redispatch cognitive closed loop, together with a Target Dispatch Algorithm (TDA) and a hierarchical command-and-scheduling method, to address multi-radar, multi-target, and multi-task scheduling in saturation attack scenarios. Methods The cooperative scheduling problem is first decoupled into dispatch-oriented network-level multidimensional resource coordination and hierarchical-command-based node-level adaptive heterogeneous task scheduling. An environment perception layer establishes a dynamic uncertainty model based on the Bayesian Cramér-Rao Lower Bound (BCRLB) and radar detection probability to jointly characterize target threat levels and radar operating states. The network coordination layer then adopts an adaptive weighted dispatch model based on comprehensive combat effectiveness to achieve dynamic target allocation while constructing a scalable execution-redispatch cognitive closed loop. To solve the resulting large-scale, nonlinear, multi-constraint generalized bipartite graph matching problem, the proposed TDA, an MMAS-based algorithm incorporating feasibility-rule-based constraint handling, is developed as a constructive solution. At the node level, Hierarchical Q-Learning (HQL) is implemented through a serial dual-Q-table implementation for distributed heterogeneous task scheduling. Using task proportion control as the hierarchical subgoal, the proposed method transforms upper-level operational intent into lower-level beam dwell scheduling, enabling adaptive, long-term, and interpretable execution of heterogeneous tasks with complex dependency relationships. Results and Discussions A simulated point-defense scenario against UAV swarm and loitering munition saturation attacks is established using three networked homogeneous S-band medium-range phased-array radars to counter 200 high-speed maneuvering targets. For network-level coordination, the proposed TDA replaces conventional penalty functions with a hierarchical solution framework based on feasibility-rule constraint handling. By exploiting prior model information, TDA achieves higher solution quality and faster convergence than the Max-Min Ant System (MMAS), Artificial Bee Colony (ABC), and Genetic Algorithm (GA) (Fig. 3). Although computational complexity increases, the execution time remains well within the dispatch cycle, improving solution quality with only millisecond-level computational overhead while satisfying real-time operational requirements (Fig. 4). For node-level scheduling, HQL employs hierarchical macro- and micro-level decisions to ensure policy consistency. Supported by an internal dense transfer-reward mechanism, HQL achieves higher learning efficiency and better long-term policy quality than Q-Learning (QL) and the Priority-Based Method (PBM) (Fig. 5). The hierarchical serial dual-Q-table framework maintains balanced task proportions, maximizes comprehensive combat effectiveness, and improves resource utilization (Fig. 6). Furthermore, comparisons of the target track-loss rate and mean tracking error show that HQL achieves the lowest mean tracking error by prioritizing high-quality tracking tasks, despite a moderately higher target track-loss rate, demonstrating superior long-term scheduling capability (Fig. 7). Conclusions The proposed hybrid architecture integrating network-level centralized dynamic target allocation with node-level distributed heterogeneous task scheduling effectively addresses the limitations of rigid dynamic coordination and insufficient heterogeneous task scheduling capability in existing networked radar systems. The proposed framework enables real-time cooperative scheduling of search, confirmation, and tracking tasks during large-scale, high-speed saturation attacks, thereby improving the operational capability of the air defense early warning system. Simulation results demonstrate improvements in applicable processing scale, environmental adaptability, long-term scheduling capability, scalability, and interpretability. Future work will explore online learning to reduce the discrepancy between offline training and online deployment and will further extend the architecture by integrating weapon-target assignment to support unified early warning and fire-control systems.
- Saturation attack,
- Networked radar,
- Target allocation,
- Task scheduling,
- Dispatch mechanism,
- Hierarchical reinforcement learning

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References(26)

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