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相关概念视频

Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
The process of harmonizing these impedances begins with a clear understanding of the input and output signals. Once these signals are known, the...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Polar Coordinates: Problem Solving01:27

Polar Coordinates: Problem Solving

Directional radiation patterns are central to antenna analysis, as they illustrate how signal strength varies with direction. These patterns are often modeled using polar plots, where the radial distance from the origin represents signal intensity at a given angle. A commonly used idealized form is the four-lobed rose curve, which captures the concept of directional beams in a simplified mathematical form.The four-lobed rose curve, described by r = cos⁡(2θ), features four symmetric lobes, each...

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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 Liu1,2, Wu Zhou3, Dongbao Qin3

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

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
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.

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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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.