Abstract
In this work, combined calibration and DoA estimation is approached as an extension of the formulation for the Single Measurement Vector (SMV) model of self-calibration to the Multiple Measurement Model (MMV) case. By taking advantage of multiple snapshots, a modified nuclear norm minimization problem is proposed to recover a low-rank larger dimension matrix. We also give the definition of a linear operator for the MMV model, and give its corresponding matrix representation to generate a variant of a convex optimization problem. In order to mitigate the computational complexity of the approach, singular value decomposition (SVD) is applied to reduce the problem size. The performance of the proposed methods are demonstrated by numerical simulations.
| Original language | English (US) |
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| Title of host publication | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538612514 |
| DOIs | |
| State | Published - Mar 9 2018 |
| Event | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao Duration: Dec 10 2017 → Dec 13 2017 |
Publication series
| Name | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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| Volume | 2017-December |
Conference
| Conference | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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| City | Curacao |
| Period | 12/10/17 → 12/13/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.