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Title: Enhancing Near-Field Holographic Imaging with Predicted Object Position
In current near-field holographic imaging, a circular region is scanned using an array of transmitting and receiving antennas over a narrow frequency band. This makes the data acquisition system slow, complex, bulky, and costly. Reducing the number of receiver antennas and using a narrower frequency band can significantly reduce the cost and complexity of the data acquisition system. To do so, we propose a method that uses prior knowledge about the object position, obtained by applying a neural network algorithm, called convolutional neural network (CNN), to the scattered field responses. This prior knowledge is then used to add a new regularization term to the cost function that is minimized in near-field holography.  more » « less
Award ID(s):
1920098
PAR ID:
10436582
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting July 23–28, 2023 • Portland, Oregon, USA
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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