IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516

この記事には本公開記事があります。本公開記事を参照してください。
引用する場合も本公開記事を引用してください。

Device-free Targets Tracking with Sparse Sampling: A Kronecker Compressive Sensing Approach
Sixing YANGYan GUODongping YUPeng QIAN
著者情報
ジャーナル 認証あり 早期公開

論文ID: 2018DRP0010

この記事には本公開記事があります。
詳細
抄録

We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.

著者関連情報
© 2019 The Institute of Electronics, Information and Communication Engineers
feedback
Top