Sparsity-based collaborative sensing in a scalable wireless network

Shuimei Zhang; Ammar Ahmed; Yimin D. Zhang

doi:10.1117/12.2521243

13 May 2019 Sparsity-based collaborative sensing in a scalable wireless network

Shuimei Zhang, Ammar Ahmed, Yimin D. Zhang

Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 1098904 (2019) https://doi.org/10.1117/12.2521243
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Shuimei Zhang, Ammar Ahmed, and Yimin D. Zhang "Sparsity-based collaborative sensing in a scalable wireless network", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 1098904 (13 May 2019); https://doi.org/10.1117/12.2521243

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