无网状搜索SR-STAP用于基于元启发算法的空中雷达
Yunfei Hou1, Yingnan Zhang1, Wenzhu Gui1
1The State Key Laboratory on Integrated Optoelectronics, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究增强了稀疏恢复的时空自适应处理 (SR-STAP),以更好地抑制杂乱. 将粒子群优化和灰狼优化 (PSO-GWO-STAP) 结合起来,有效地解决了非线性杂乱环境中的离网问题.
科学领域:
- 信号处理 信号处理
- 雷达系统 雷达系统
- 优化算法 优化算法
背景情况:
- 稀疏恢复 (SR) 时空自适应处理 (STAP) 提供了优秀的杂乱抑制与有限的样本.
- 空间多普勒形状中的非线性杂乱会导致离网效应,降低SR-STAP的性能.
研究的目的:
- 在SR-STAP中使用元启发式算法 (MH) 调查无网格搜索,以消除离网效应.
- 评估和改进SR-STAP在非线性杂乱环境中的性能.
主要方法:
- 应用遗传算法 (GA),差异进化 (DE),粒子群优化 (PSO) 和灰狼优化 (GWO) 进行SR-STAP.
- 通过将PSO和GWO结合起来,开发了一种改进的PSO-GWO-STAP算法,以提高全球优化.
- 使用对杂乱分布的先验知识,增强了健身功能.
主要成果:
- MH-STAP方法比传统算法更准确地估计了杂乱子空间.
- 与其他MH-STAP方法相比,PSO-STAP和GWO-STAP表现出优异的杂乱抑制.
- 改进的PSO-GWO-STAP算法在解决离网问题方面明显优于单个MH-STAP方法.
结论:
- 在SR-STAP中,元启发式算法有效地减轻了离网效应.
- 结合PSO-GWO方法和改进的健身功能,提供卓越的杂乱抑制性能.
- 拟议的PSO-GWO-STAP方法为SR-STAP在具有挑战性的非线性杂乱条件下提供了强大的解决方案.
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