摘要：

组网雷达干扰资源分配问题是典型的非确定性多项式(NP)问题, 需采用各种优化算法对其进行求解。针对传统干扰资源分配优化算法计算速度慢、适应性差的问题, 系统梳理干扰资源分配智能优化算法的研究进展。首先构建组网雷达干扰资源分配的数学模型及求解框架, 分析其求解难点, 强调智能优化算法在计算效率、全局优化能力、鲁棒性等方面的明显优势; 然后以遗传算法(GA)、粒子群优化算法(PSO)、蚁群算法(ACA)及其各种改进算法为典型代表, 对智能优化算法在组网雷达干扰资源分配中的实施流程、求解效果、优缺点等进行详细分析, 并对融合算法及其他仿生/机器学习智能优化算法在该领域的应用进行总结归纳, 从适应性、收敛性、全局搜索能力等方面对比分析了各类算法的优劣, 充分展现了智能优化算法在该应用方向上的发展现状; 最后结合当前组网雷达干扰资源分配所面临的多重挑战, 从算法对比、寻优速度、融合创新与动态适应性4个方面对智能优化算法未来的发展方向做出了展望。本文研究内容对组网雷达干扰资源分配中智能优化算法的研究及工程实践具有重要的参考价值。

关键词: 组网雷达, 干扰资源分配, 数学模型, 智能优化算法, 协同干扰

Abstract:

The networked radar jamming resource allocation is a typical Non-deterministic Polynomial (NP) problem and a significant challenge, requiring the use of various optimization algorithms to solve it. To address the issues of low computational speed and poor adaptability in traditional jamming resource allocation optimization algorithms, advancements in intelligent optimization algorithms in this field are reviewed. First, a mathematical model and a solution framework for networked radar jamming resource allocation are constructed. The difficulties in solving this model are analyzed, and the advantages of intelligent optimization algorithms in terms of computational efficiency, global optimization capability, and robustness are emphasized. Subsequently, using the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Algorithm (ACA), and various improved algorithms as typical examples, the implementation processes, solution effectiveness, and strengths and weaknesses of intelligent optimization algorithms in networked radar jamming resource allocation are analyzed in detail. Additionally, the application of fusion algorithms and other bionic/machine learning-based intelligent optimization algorithms in this field is summarized, and the advantages and disadvantages of various algorithms are compared and analyzed in terms of aspects such as adaptability, convergence, and global search capability, thus fully demonstrating the current development status of intelligent optimization algorithms. Finally, considering the multiple challenges currently encountered in networked radar jamming resource allocation, future development directions of intelligent optimization algorithms are proposed from four perspectives: algorithm comparison, optimization speed, fusion innovation, and dynamic adaptability. This study provides a valuable reference for the research and practical engineering of intelligent optimization algorithms in networked radar jamming resource allocation.

Key words: networked radar, jamming resource allocation, mathematical model, intelligent optimization algorithm, cooperative jamming