基于压缩感知的免携带设备双目标定位算法

发布时间：2018-03-20 16:10

本文选题：免携带设备目标定位(DFL)　切入点：压缩感知　出处：《电子与信息学报》2014年04期 　论文类型：期刊论文

【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。
[Abstract]:In order to solve the problem of time-varying radio-frequency signal in multi-target location, the location information can be obtained by DFL without any equipment. This paper combines fingerprint method with fingerprint method. In this paper, a novel localization algorithm based on compression sensing is proposed, in which the central probabilistic coverage model is used to establish the mapping relationship between a single object radio frequency map and a dual target radio frequency map. In order to solve the problem of sharp increase of off-line training caused by the increase of target number in fingerprint method, K-means clustering method is used to classify the radio-frequency map of two targets, and the range of location area is reduced by class matching. Finally, the localization problem is transformed into the problem of sparse signal reconstruction, which improves the localization accuracy. The experimental results show that compared with the compressed sensing algorithm based on wireless tomography, the localization problem is more accurate than the compression sensing algorithm based on wireless tomography. The algorithm can achieve higher positioning accuracy and higher real-time performance.
【作者单位】：上海大学通信与信息工程学院;
【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
【分类号】：TN911.7

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