宽带DOA估计的高效支向量回归使用遗传算法
Yonghong Zhao1,2, Gang Zheng1,2, Junlong Wang1
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究引入了一种高效的支向量回归 (SVR) 模型,由遗传算法 (GA) 优化,用于对宽带信号的高精度到达方向 (DOA) 估计. 该方法显著降低了计算负载并提高了准确性,特别是在资源有限的环境中.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 阵列信号处理 阵列信号处理
背景情况:
- 高精度的到达方向 (DOA) 估计对于雷达和通信系统至关重要.
- 现有的方法经常面临宽带信号和计算复杂性的挑战.
研究的目的:
- 开发一个高效和高性能宽带DOA估计算法.
- 为了减少资源有限的场景的计算负载和存储需求.
主要方法:
- 提出了一种由遗传算法 (GA) 优化的高效支向量回归 (SVR) 架构.
- 利用双边相关性转换 (TCT) 算法,使用参考频率数据进行高效的网络训练.
- 实现了预处理步骤,以减少数组共变矩阵的维度,利用其并联对称性和元素特征.
主要成果:
- 实现了宽带DOA的高估计性能和概括能力.
- 通过保持不变的输入特征维度,无论信号带宽如何,显著减少了训练时间和系统存储容量.
- 通过实验验证,与现有方法相比,证明了更高的效率和性能.
结论:
- 拟议的GA优化SVR方法为宽带DOA估计提供了高效和有效的解决方案.
- 减小维度技术对于资源有限的应用中宽带和超宽带信号特别有利.
- 该算法在性能,培训效率和存储要求方面显示出显著的优势.
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