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.
A radio frequency （RF） link selection method based on Bayesian compressive sensing （BCS） is presented to effi-ciently avoid selecting redundant links with random measurement for compressed RF tomography .Firstly ,link entropy and redundan-cy 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 measure-ment 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 .
出处
《电子学报》
EI
CAS
CSCD
北大核心
2013年第12期2507-2512,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.61074167)
