CAJ | 학술논문

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。
면휴대설비적목표정위(DFL)불수요목표휴대임하설비취능획취위치신식，침대현유산법재다목표정위중존재적인사빈신호시변특성인기적문제，해문결합지문법，제출료기우압축감지적면휴대설비쌍목표정위산법。해산법채용중심개솔복개모형건립단목표사빈지도도쌍목표사빈지도적영사관계，해결지문법유우목표수적증가인기적리선훈련량취증적문제。병채용K-means취류방법대쌍목표사빈지도진행분류，통과류필배축소정위구역적범위，강저정위산법적복잡도。최후이용압축감지적방법，장정위문제전화성희소신호적중구문제，제고료정위정도。실험결과표명，여기우무선층석성상적압축감지정위산법상비，해산법능체도교고적정위정도，차실시성경고。
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.