Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning

• URL: http://arxiv.org/abs/2004.11637v2
• Date: Tue, 2 Jun 2020 07:40:35 GMT
• Title: Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning
• Authors: Ahmet M. Elbir and Kumar Vijay Mishra
• Abstract summary: 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. 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. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL
• Score: 22.51807198305316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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