论文标题
跨任意传感器几何形状的稀疏阵列选择，并进行深度传输学习
Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning
论文作者
论文摘要
稀疏的传感器阵列选择是在许多工程应用中出现的，必须从有限数量的阵列元素中获得最大的空间分辨率。最近的研究表明，通过使用深度学习网络替换常规优化和贪婪的搜索方法来降低阵列选择的计算复杂性。但是，实际上，不可用的训练数据足够且精心校准的训练数据，更重要的是，对于任意阵列配置。为了解决这个问题，我们采用了深入的转移学习方法（TL）方法，其中我们使用源传感器阵列的数据训练深卷卷神经网络（CNN），可随时使用校准数据，并重复使用此预先训练的CNN，以针对不同的，充满数据的目标数字几何来执行稀疏阵列选择。具有均匀矩形和圆形阵列的数值实验表明，在目标模型上，TL-CNN的性能提高了，而在同一模型的数据不足的CNN中，TL-CNN的性能提高。特别是，我们的TL框架可提供大约20％的传感器选择精度，并提高到达方向估计误差的10％。
Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error.
