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Proceedings Paper

Sparsity-based collaborative sensing in a scalable wireless network
Author(s): Shuimei Zhang; Ammar Ahmed; Yimin D. Zhang
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Paper Abstract

In this paper, we propose a collaborative sensing scheme for source localization and imaging in an unmanned aerial vehicle (UAV) network. A two-stage image formation approach, which combines the robust adaptive beamforming technique and sparsity-based reconstruction strategy, is proposed to achieve accurate multi-source localization. In order to minimize the communication traffic in the UAV network, each UAV node only transmits the coarse-resolution image, in lieu of the large volume of raw sampled data. The proposed method maintains the robustness in the presence of model mismatch while providing a high-resolution image.

Paper Details

Date Published: 13 May 2019
PDF: 8 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 1098904 (13 May 2019); doi: 10.1117/12.2521243
Show Author Affiliations
Shuimei Zhang, Temple Univ. (United States)
Ammar Ahmed, Temple Univ. (United States)
Yimin D. Zhang, Temple Univ. (United States)

Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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