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CAWE-ACNN算法用于配对传感器阵列的自适应光束成形

Fulai Liu^1,2, Wu Zhou³, Dongbao Qin³

¹Laboratory of GNSS Anti-Jamming Technology, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|September 14, 2024

概括

这项研究介绍了coprime传感器阵列与加权注意力 (CAWE) 算法,一个注意力卷积神经网络 (ACNN) 强大的自适应束成型. CAWE-ACNN算法提高了信号与干扰加噪声比 (SINR) 的性能和计算效率.

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:

适应式光束成型对于增强传感器阵列中所需信号至关重要.
传统方法与复杂的干扰环境和计算负载作斗争.
同级传感器阵列在自由度方面具有优势,但需要复杂的处理.

研究的目的:

开发一个强大的自适应束形算法,用于 coprime 传感器阵列.
通过使用深度学习来提高信号与干扰加噪声比 (SINR) 的性能.
为了实现高计算效率的梁成形重量向量的估计.

主要方法:

提出了一个注意力卷积神经网络 (ACNN) 模型,结合了空间和通道注意力单元.
一个新的干扰加噪声共变矩阵重建算法被用于ACNN模型训练.
该ACNN使用来自 coprime 传感器阵列的样本信号进行训练,以输出波束成形重量.

主要成果:

拟议的CAWE-ACNN算法显示了SINR性能的显著改进.
与现有方法相比,该算法实现了高计算效率.
模拟结果验证了基于ACNN的光束成形的稳定性和有效性.

结论:

该CAWE-ACNN算法提供了一个强大的和高效的解决方案,适应性光束成形在coprime传感器阵列.
在CNN中整合注意力机制有效地提高了光束成形性能.
这种深度学习方法为未来的数组信号处理应用提供了有希望的方向.

关键词:

注意力卷积神经网络注意力卷积神经网络同一个主要的传感器阵列.强大的自适应式光束成型.估计重量向量的估计.

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