基于离散动态网格的信号强度指纹库压缩感知定位方法

Method of compressed sensing positioning based on discrete dynamic grid and fingerprint feature library of signal intensity

矿井目标定位已基本完成从RFID技术向TOA或RSS测距三点解算方法过渡,但电磁波测距三点解算定位方法面临着对硬件系统要求苛刻、对井下电磁噪声较为敏感等难以克服的问题。将压缩感知理论与定位结合可以很好地解决这一难题,提出了一种基于离散动态网格的信号强度指纹库压缩感知定位方法。首先将定位区域离散化处理,通过巷道的网格化,离线测量各网格位置处的信号强度特征,构建特征指纹库;然后在线通过L近邻动态节点选择方法确定目标所在范围实现区域定位;根据指纹特征库构建冗余字典以及区域定位的结果构建测量矩阵;最后通过压缩感知算法重构信号强度场矩阵,实现目标精确定位。实验结果表明,该方法提高了定位精度,减小了通信开销,应用在矿井环境有很好的定位效果。

Mine target positioning has basically completed the transition from RFID technology to TOA or RSS ranging three-point solution method,but the three-point solution positioning method of electromagnetic wave ranging is faced with difficult problems that are difficult to be overcome in hardware systems and sensitive to electromagnetic noise in mine roadway.However,combining the theory of compressed sensing with localization can solve this problem very well.In this paper,a method of compressed sensing positioning based on discrete dynamic grid and the fingerprint feature library of signal intensity is proposed.Firstly,the positioning area is discretely meshed,and the signal strength characteristics at each grid position are measured offline to construct a feature fingerprint database.Then,the L-nearest neighbor dynamic node selection method is used to determine the target positioning to achieve regional positioning.Finally,the measurement matrix is constructed based on the fingerprint dictionary and the results of regional positioning,and the signal intensity field matrix is reconstructed by the compressed sensing algorithm to achieve accurate target location.The experimental results show that the method has a good positioning effect in the roadway environment with a higher positioning accuracy and less communication overhead.