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Updated: Jun 27, 2025

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy
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在具有坏数据的顺式偏振敏感数组中,基于强大的张量DOA和偏振估计

Xiaoyu Lan1,2, Lai Jiang3, Shuang Ma1

  • 1School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括

本研究引入了一种新的张量变量稀疏贝叶斯学习方法,以准确估计到达方向 (DOA) 和偏振参数,即使有部分受损的传感器阵列. 该方法有效地处理不良数据并恢复丢失的信息,以进行可靠的信号参数估计.

关键词:
列向量检测检测 列向量检测符合性极化敏感阵列的阵列抵达方向和极化参数估计估计方向.张量器是一个张量器.变化的稀疏贝叶斯式学习.

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