Sparse Array Optimization Based on Double-Deck ADMM for Near-Field MMW 3-D Reconstruction of Human Bodies

In this letter, to address the problems of high cost and system complexity associated with uniform array antennas for human body inspection, a novel near-field sparse array manifold optimization algorithm is proposed based on a double-deck alternating direction method of multipliers (DD-ADMM) framew...

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Veröffentlicht in:IEEE antennas and wireless propagation letters Jg. 23; H. 9; S. 2568 - 2572
Hauptverfasser: Hu, Zhongwei, Chen, Yingjie, Song, Hao, Yang, Lei, Sun, Zhaoyang, Xing, Mengdao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Alternating direction method of multipliers
Antenna arrays
Arrays
Closed form solutions
Complexity
Costs
Decks
Excitation
Human body
Image reconstruction
Inspection
Millimeter waves
millimeter-wave radar imagery
Near fields
near-field sparse array manifold
Optimization
Radar
Radar data
Radar imaging
Security
security inspection
Sidelobes
Spatial data
Vectors
ISSN:1536-1225, 1548-5757
Online-Zugang:Volltext
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Zusammenfassung:In this letter, to address the problems of high cost and system complexity associated with uniform array antennas for human body inspection, a novel near-field sparse array manifold optimization algorithm is proposed based on a double-deck alternating direction method of multipliers (DD-ADMM) framework. Unlike conventional optimizations that requires manually adjusting the sparsity threshold of weight excitation, the proposed algorithm incorporates auxiliary variables to decouple the weight excitation from the objective function. Furthermore, an augmented Lagrange expression is constructed under the ADMM framework, so that the weight excitation can be automatically adjusted to obtain a closed-form solution for sparse elements. In comparison with the single-deck ADMM structure, the proposed algorithm mitigates the computational complexity associated with solving the joint minimization problem, which involves reducing the <inline-formula><tex-math notation="LaTeX"> \ell _{1}</tex-math></inline-formula>-norm of weight excitation and minimizing the difference between peak sidelobe levels and auxiliary functions through the nested DD-ADMM framework. In addition, the proposed algorithm handles sparse spatial-domain echo data, eliminating the need for interpolation and padding. Finally, the superiority of the algorithm is demonstrated through both simulated and raw millimeter-wave radar data for near-field human body imagery.
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ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2024.3399752