A Method of Link Selection for Radio Frequency Tomography with Bayesian Compressive Sensing

HAO Xiao-xi; YANG Zhi-yong; GUO Xue-mei; WANG Guo-li

doi:10.3969/j.issn.0372-2112.2013.12.030

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A Method of Link Selection for Radio Frequency Tomography with Bayesian Compressive Sensing

更新时间：2025-07-16

- A Method of Link Selection for Radio Frequency Tomography with Bayesian Compressive Sensing
- Acta Electronica Sinica Vol. 41, Issue 12, Pages: 2507-2512(2013)
- 作者机构：
  
  1. 中山大学信息科学与技术学院,广东,广州,510006
  2. 五邑大学机电工程学院,广东,江门,529020
  3. 中山大学信息科学与技术学院,广东,广州,510006
  4. 五邑大学机电工程学院,广东,江门,529020
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61074167)
- DOI：10.3969/j.issn.0372-2112.2013.12.030
  CLC： TN929.5TP212.9
- Published：2013
- 稿件说明：
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HAO Xiao-xi, YANG Zhi-yong, GUO Xue-mei, et al. A Method of Link Selection for Radio Frequency Tomography with Bayesian Compressive Sensing[J]. Acta Electronica Sinica, 2013, 41(12): 2507-2512.
DOI：

HAO Xiao-xi, YANG Zhi-yong, GUO Xue-mei, et al. A Method of Link Selection for Radio Frequency Tomography with Bayesian Compressive Sensing[J]. Acta Electronica Sinica, 2013, 41(12): 2507-2512. DOI： 10.3969/j.issn.0372-2112.2013.12.030.

摘要

针对压缩射频层析成像中随机链路选取策略无法有效避免选取冗余链路，本文提出一种利用贝叶斯压缩传感实现的射频链路选择策略.该策略首先通过定义链路冗余度和链路熵，建立表示射频链路信息量与冗余度关系的最小熵链路决策模型，其次将贝叶斯压缩传感所提供的自适应投影测量框架与最小熵链路决策模型结合，最终实现链路选择和目标估计.环境目标定位实验表明，所提出的射频链路选择策略是有效的和可行的.与随机选择策略比较，其能够有效减少冗余或无关链路的选取，提高传感效率.

Abstract

A radio frequency (RF) link selection method based on Bayesian compressive sensing (BCS) is presented to efficiently avoid selecting redundant links with random measurement for compressed RF tomography.Firstly

link entropy and redundancy are defined

and then the minimum differential entropy link decision model is established

which indicates the relationship between the RF link information and redundancy.Secondly

the presented model is combined with BCS based adaptive projections measurement framework

to realize link selection and target estimation.Localization experiments illustrate efficiency and feasibility of the proposed method

which can efficiently avoid the selection of redundant links and improve the sensing efficiency compared with the random selection method.

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