通信学报 ›› 2016, Vol. 37 ›› Issue (5): 152-164.doi: 10.11959/j.issn.1000-436x.2016103

• 学术论文 • 上一篇    下一篇

仅依赖连通度的压缩感知多目标定位方法

刘磊1,张建军1,2,陆阳1,2,卫星1,2,韩江洪1,2   

  1. 1 合肥工业大学计算机与信息学院,安徽 合肥230009
    2 教育部安全关键工业测控工程研究中心,安徽 合肥 230009
  • 出版日期:2016-05-25 发布日期:2016-06-01
  • 基金资助:
    国家自然科学基金资助项目;国家国际科技合作专项基金资助项目

Multiple targets localization via compressive sensing from mere connectivity

Lei LIU1,Jian-jun ZHANG1,2,Yang LU1,2,Xing WEI1,2,Jiang-hong HAN1,2   

  1. 1 School of Computer & Information,Hefei University of Technology,Hefei 230009,China
    2 Engineering Research Center of Safety Critical Industrial Measurement & Control Technology,Ministry of Education,Hefei 230009,China
  • Online:2016-05-25 Published:2016-06-01
  • Supported by:
    The National Natural Science Foundation of China;The National Foundation of International Cooperation in Science and Technology

摘要:

提出仅依赖连通度的多目标定位方法,将多目标定位问题转化为基于压缩感知的稀疏向量重构,解决室内参照物高密度分布的目标定位问题。定位方法仅以连通度为观测值,运用最小化l1范数法求解目标位置。当观1-测数据压缩为1 bit时,提出半正定松弛和不动点迭代法结合的目标求解算法。根据仿真实验结果,与MDS-MAP、DV-Hop和RSS-CS方法进行比较得出,仅连通度的非1-bit和1-bit量化的CS定位方法的平均定位误差小于1个网格,且2种方法占用的比特数只相当于RSS定位方法占用比特数的14116

关键词: 多目标定位, 压缩感知, 连通度, 1-bit量化

Abstract:

A multiple targets localization method was proposed from mere connectivity,and the multiple targets posi-tioning problem was converted to sparse vector resolving by compressive sensing theory,which was applied to the indoor localization of intensive references distribution.The connectivity to the references was collected as the only measurement data,and targets locations were figured out by minimum l1-norm algorithm.When measurement data was compressed tol 1 bit,the fixed point iteration algorithm combined with semi-definite relax was proposed to figure out targets locations.As for the simulation results,compared with MDS-MAP,DV-Hop and RSS-CS algorithms,the average location error is less than 1 grid by the mere connectivity of N -bit and 1-bit quantization CS localization,of which the occupied bit quan-tity are reduced to less than 4 times and 16 times of RSS localization observation value respectively.

Key words: multiple targets localization, compressive sensing, connectivity, 1-bit quantization

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