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Phaseless Terahertz Coded-Aperture Imaging for Sparse Target Based on Phase Retrieval Algorithm.

Long Peng¹, Chenggao Luo², Bin Deng³

¹College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China. penglong_nudt@163.com.

Sensors (Basel, Switzerland)

|October 27, 2019

Summary

This summary is machine-generated.

Phaseless terahertz coded-aperture imaging (PL-TCAI) achieves high-resolution imaging using a compact system. A new sparse Wirtinger flow algorithm with optimal stepsize (SWFOS) significantly reduces required measurement samples for PL-TCAI.

Keywords:

Wirtinger flow coded-aperture imaging optimal stepsize phase retrieval phaseless imaging terahertz

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Area of Science:

Computational Imaging
Terahertz Technology
Radar Systems

Background:

Phaseless terahertz coded-aperture imaging (PL-TCAI) is a radar computational imaging technique enabling forward-looking, high-resolution imaging without relative motion.
Existing PL-TCAI methods often require excessive measurement samples for accurate target reconstruction due to reliance on echo signal intensity.

Purpose of the Study:

To propose a more reasonable and compact architecture for the PL-TCAI system.
To develop an efficient phase retrieval algorithm that reduces the number of measurement samples required for PL-TCAI.

Main Methods:

Derivation of the PL-TCAI imaging model based on random frequency-hopping signals.
Proposal of a sparse Wirtinger flow algorithm with optimal stepsize (SWFOS) leveraging target sparsity.
Implementation of SWFOS involving support recovery, spectral initialization, gradient descent, hard thresholding, and stepsize optimization.

Main Results:

Numerical simulations demonstrate the effectiveness of the SWFOS algorithm for phase retrieval in PL-TCAI.
The SWFOS algorithm significantly reduces the number of measurement samples needed for imaging compared to conventional methods.

Conclusions:

The proposed SWFOS algorithm offers improved performance and sample efficiency for PL-TCAI.
This advancement contributes to more practical and resource-efficient terahertz computational imaging systems.