Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning
| dc.authorid | Elbir, Ahmet M./0000-0003-4060-3781 | |
| dc.authorwosid | Elbir, Ahmet M./X-3731-2019 | |
| dc.contributor.author | Elbir, Ahmet M. | |
| dc.contributor.author | Mishra, Kumar Vijay | |
| dc.date.accessioned | 2021-12-01T18:48:21Z | |
| dc.date.available | 2021-12-01T18:48:21Z | |
| dc.date.issued | 2021 | |
| dc.department | [Belirlenecek] | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.1109/TCCN.2020.2999811 | |
| dc.identifier.endpage | 264 | en_US |
| dc.identifier.issn | 2332-7731 | |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.scopus | 2-s2.0-85102319889 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.startpage | 255 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/TCCN.2020.2999811 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12684/10515 | |
| dc.identifier.volume | 7 | en_US |
| dc.identifier.wos | WOS:000626515700021 | en_US |
| dc.identifier.wosquality | Q1 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Ieee-Inst Electrical Electronics Engineers Inc | en_US |
| dc.relation.ispartof | Ieee Transactions On Cognitive Communications And Networking | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Sensor arrays | en_US |
| dc.subject | Geometry | en_US |
| dc.subject | Direction-of-arrival estimation | en_US |
| dc.subject | Training | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Training data | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | direction-of-arrival estimation | en_US |
| dc.subject | sensor placement | en_US |
| dc.subject | sparse arrays | en_US |
| dc.subject | transfer learning | en_US |
| dc.title | Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning | en_US |
| dc.type | Article | en_US |
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