Collaborative Interference Resource Allocation Method Based on Improved Secretary Bird Algorithm

LI Yibing; SUN Liuqing; QI Changlong

doi:10.11999/JEIT240709

Volume 47 Issue 5

May 2025

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LI Yibing, SUN Liuqing, QI Changlong. Collaborative Interference Resource Allocation Method Based on Improved Secretary Bird Algorithm[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1494-1504. doi: 10.11999/JEIT240709

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LI Yibing, SUN Liuqing, QI Changlong. Collaborative Interference Resource Allocation Method Based on Improved Secretary Bird Algorithm[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1494-1504. doi: 10.11999/JEIT240709

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Collaborative Interference Resource Allocation Method Based on Improved Secretary Bird Algorithm

doi: 10.11999/JEIT240709 cstr: 32379.14.JEIT240709

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

Received Date: 2024-08-13
Rev Recd Date: 2025-04-01

Available Online: 2025-04-23

Publish Date: 2025-05-01

Abstract

Abstract

Objective In the complex electromagnetic environment of Networked Radars (NR), efficiently utilizing limited interference resources to reduce enemy detection capabilities and support successful penetration remains a critical challenge. Existing heuristic algorithms, while partially effective, do not jointly optimize interference patterns, beams, and power resources in multi-beam systems, limiting their applicability in penetration scenarios. To address this limitation, this study proposes an interference resource allocation strategy based on the Improved Secretary Bird Optimization Algorithm (ISBOA). The proposed strategy minimizes detection probability by integrating Cauchy mutation and global collaborative control, enabling the joint optimization of interference patterns, beams, and power across multiple jammers. This approach ensures rational resource allocation, enhances search capability, and improves convergence accuracy, thereby meeting the demands of penetration scenarios. The findings provide a novel solution for interference resource allocation in multi-beam systems against NR. Methods This study models the complex interference resource allocation problem as a multi-constrained nonlinear mixed-integer programming problem and addresses it using an improved intelligent optimization algorithm. A mixed-integer programming model incorporating interference patterns, beams, and power resources is established, with the detection and fusion probability of networked radar as the performance evaluation metric. The model accounts for the dynamic interactions between radars and jammers, as well as the pulse compression gains of various interference patterns. To overcome the limitations of the traditional Secretary Bird Optimization Algorithm (SBOA) in handling discrete variables and complex constraints, the study integrates Cauchy mutation and global collaborative control strategies. Cauchy mutation leverages its long-tail characteristics to enhance the algorithm’s global search capability, reducing the risk of convergence to local optima. The global collaborative control strategy incorporates penalty factors to ensure compliance with multi-variable constraints, enabling the simultaneous optimization of discrete and continuous variables. Results and Discussions This study presents an innovative interference resource allocation method for multi-beam jamming systems targeting networked radar, leveraging the ISBOA. By integrating Cauchy mutation and global cooperative control strategies, ISBOA significantly enhances optimization performance. Simulation results indicate that ISBOA outperforms other algorithms, including the original SBOA, Harris Hawks Optimizer (HHO), and Sparrow Search Algorithm (SSA). In a scenario with six jammers and eight radars, ISBOA achieved an optimal function value of 0.6095, which is notably lower than 0.8158 (SBOA), 1.2666 (HHO), and 1.3679 (SSA) (Fig. 4). Moreover, ISBOA demonstrated faster convergence and greater stability across 50 independent experiments, yielding an average optimal function value of 0.6892 (Fig. 5) and a convergence error of 0.1449 (Fig. 6). ISBOA’s joint optimization of interference patterns, beams, and power resources enables more efficient allocation of jamming resources and reduces the detection probability of networked radar. This advantage is further validated across various scenarios, where ISBOA consistently outperformed other algorithms in solution quality and computational efficiency (Fig. 8). The experimental results highlight ISBOA’s robustness and adaptability, demonstrating its potential for application in complex battlefield environments. Conclusions This study proposes an optimization method for interference resource allocation in multi-beam jamming systems targeting networked radar scenarios, utilizing ISBOA. A mixed-integer programming model integrating interference patterns, beams, and power resources is developed. ISBOA incorporates Cauchy mutation and global cooperative control strategies to enhance global search capability and stability. Simulation results demonstrate that ISBOA outperforms the original SBOA, HHO, and SSA in terms of convergence speed and search efficiency. ISBOA exhibits superior stability and enables more rational allocation of interference resources, effectively reducing the detection probability of networked radar. Moreover, ISBOA demonstrates strong adaptability and robustness across various scenarios, providing an effective solution for interference resource allocation in complex battlefield environments.
- Networked radar,
- Collaborative interference,
- Resource allocation,
- Secretary Bird Optimization Algorithm(SBOA)

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References(22)

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