Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning
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Dosyalar
Tarih
2021
Yazarlar
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Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Sensor arrays, Geometry, Direction-of-arrival estimation, Training, Deep learning, Training data, Deep learning, direction-of-arrival estimation, sensor placement, sparse arrays, transfer learning
Kaynak
Ieee Transactions On Cognitive Communications And Networking
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
7
Sayı
1
