NDLI: A robust sparse optimization for pattern synthesis with unknown manifold error

Content Provider	IEEE Xplore Digital Library
Author	Jiazhou Liu Zhiqin Zhao Jinguo Wang Qing Huo Liu
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA (Qing Huo Liu) \|\| Dept. of Electr. Eng., Univ. of Electron. & Sci. Technol. of China, Chengdu, China (Jiazhou Liu; Zhiqin Zhao; Jinguo Wang)
Abstract	The performance of synthesis pattern with sparse arrays is known to degrade in the presence of errors in the array manifolds. This paper introduces a beampattern synthesis approach with uncertain manifold vectors perturbation for linear array. In order to match the desired pattern and minimize the elements simultaneously, the convex optimization of minimizing a reweighted $l_{1}-norm$ objective based on the weights of elements is proposed. The superposition sampling is used for select the elements. The excitation weights and sensor positions of an array radiating pencil beampatterns are obtained. This method is demonstrated through numerical simulations. The results show the maximally sparse array in beampattern synthesis with manifold vectors perturbation is obtained and the method is effective.
Starting Page	0099
Ending Page	0103
File Size	793961
Page Count	5
File Format	PDF
e-ISBN	9781479920358
DOI	10.1109/RADAR.2014.6875563
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Vectors Convex functions Antenna arrays Robustness
Content Type	Text
Resource Type	Article

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