期刊
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
卷 68, 期 12, 页码 8114-8126出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAP.2020.2999741
关键词
Data models; Microwave imaging; Microwave theory and techniques; Computational modeling; Cost function; Image reconstruction; Microwave measurement; Full-wave; level set; microwave inversion; model-based; pixel-based; supervised descent method (SDM)
资金
- National Key Research and Development Program of China [2018YFC0603604]
- National Science Foundation of China [61571264, 61971263]
- Guangzhou Science and Technology Plan [201804010266]
- Beijing Innovation Center for Future Chip
- Research Institute of Tsinghua, Pearl River Delta
We present a general framework of applying supervised descent method (SDM) to the pixel- and model-based full-wave inversion of microwave data for dielectric targets. SDM is a machine learning approach that utilizes the learned descent directions to reconstruct models. In this article, we study the pixel-based inversion with an online regularization scheme, and the model-based inversion with the parametric level set approach. Furthermore, an online restart scheme is studied to further reduce the data residual. In addition, we investigate the generalization ability of this machine-learning algorithm. In the numerical test, the pixel-based SDM inversion are used to process both single- or multi-frequency data, and the model-based inversion are performed on data with limited observations. Both synthetic and experimental results show good accuracy, speed, and generalization ability of this algorithm. SDM may provide us a potential way to improve reconstruction quality in nonlinear inversion through information fusion of measured data, microwave physics, and various prior information.
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