基于压缩感知的免携带设备双目标定位算法  

Bi-object Device-free Localization Based on Compressive Sensing

作　　者：刘凯[1] 余君君[1] 黄青华[1] 

机构地区：[1]上海大学通信与信息工程学院,上海200072

出　　处：《电子与信息学报》2014年第4期862-867,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61001160);上海市教委创新基金一般项目(11YZ14)资助课题

摘　　要：免携带设备的目标定位(DFL)不需要目标携带任何设备就能获取位置信息,针对现有算法在多目标定位中存在的因射频信号时变特性引起的问题,该文结合指纹法,提出了基于压缩感知的免携带设备双目标定位算法。该算法采用中心概率覆盖模型建立单目标射频地图到双目标射频地图的映射关系,解决指纹法由于目标数的增加引起的离线训练量骤增的问题。并采用K-means聚类方法对双目标射频地图进行分类,通过类匹配缩小定位区域的范围,降低定位算法的复杂度。最后利用压缩感知的方法,将定位问题转化成稀疏信号的重构问题,提高了定位精度。实验结果表明,与基于无线层析成像的压缩感知定位算法相比,该算法能达到较高的定位精度,且实时性更高。The time-varying characteristics of radio frequency signal make it difficult to practice multi-object Device-Free Localization （DFL）. A novel algorithm based on compressive sensing and fingerprint method is proposed to locate bi-object in this paper. It utilizes link-centric probabilistic coverage model to construct the mapping relationship between single object radio map and bi-object radio map, which reduces the offline train labour brought for the increased number of objects. Furthermore, K-means clustering method is taken to classify the established bi-object radio map. By comparing online measurement with the centre elements of every cluster, the possible locations of the bi-object are limited to a smaller area, which shortens the computing time. Then, compressive sensing is adopted to transform the localization problem to a sparse signal reconstruction problem. Experiments confirm that the proposed algorithm outperforms than the Radio Tomographic Imaging （RTI） based algorithm.

关 键 词：免携带设备目标定位(DFL) 压缩感知 双目标射频地图 K-MEANS聚类 

分 类 号：TN911.7[电子电信—通信与信息系统]