首页|基于RSSI超宽带室内定位锚节点选择方法

基于RSSI超宽带室内定位锚节点选择方法

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在超宽带室内定位中,由于室内环境复杂,各锚节点与定位节点间的通信会受到不同程度的干扰,干扰较大的数据会严重影响定位精度,因此需要对锚节点进行筛选。针对以上问题,提出基于接收信号强度(RSSI)超宽带室内定位锚节点选择方法。首先,采用插值法计算定位节点对各锚节点间的RSSI并采用高斯过程回归进行优化,得到初始的RSSI估计值;然后,对此估计值在锚节点位置和路径损耗因子处进行泰勒级数展开,得到具有RSSI信息的费雪矩阵,从而获得RSSI的Cramér-Rao下界(CRLB);接着,将所有锚节点选取状态组合(选择为1,不选为0)代入CRLB公式中,通过半定松弛求解CRLB的迹;最后,最小迹所对应的选取状态组合即为选择结果。实验结果表明,与未进行锚节点选择的算法相比,所提出方法在X、Y、Z方向上的定位精度分别提升37。6%、32。2%、38。8%,且该方法接近穷举法的锚节点选择结果。此外,所提出算法采用无监督的方式无需获取先验数据,具有较高的实际应用价值。
Selecting anchor node based on RSSI ultra-wideband indoor positioning
In ultra-wideband indoor positioning,due to the complex indoor environment,the communication between each anchor node and the positioning node will be subject to different degrees of interference,and the more interfered data will seriously affect the positioning accuracy,so it is necessary to screen the anchor nodes.Aiming at the above problems,an ultra-wideband indoor positioning anchor node selection method based on received signal strength indication(RSSI)is proposed.Firstly,the RSSI between the anchor nodes is calculated using the interpolation method and optimized by Gaussian process regression to obtain the initial RSSI estimation value.Secondly,this estimation value is Taylor-expanded at the anchor node position and the path loss factor to obtain the Fisher matrix with the RSSI information,and then,the Cramér-Rao lower bound(CRLB)of the RSSI is obtained.Then,all the anchor nodes are selected by the method,and the RSSI of the anchor nodes is obtained.Then,all the anchor node selected state combinations(selected as 1 and unselected as 0)are substituted into the CRLB formula,and the traces of the CRLB are solved by semidefinite relaxation.Finally,the selected state combination corresponding to the minimum trace is the selection result.The experimental results show that compared with the algorithm without anchor node selection,this method improves the positioning accuracy in X,Y,and Z directions by 37.6%,32.2%,and 38.8%,respectively,and it is close to the anchor node selection result of the exhaustive method.In addition,the proposed algorithm adopts an unsupervised approach without obtaining a priori data,which has high practical application value.

indoor positioningultra-widebandanchor node selectionreceived signal strength indicationCramér-Rao lower boundunsupervised

李冰、崔昳阳、刘宇、刘春刚、高占良

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河北师范大学中燃工学院,石家庄 050024

河北省无线传感器网络数据应用技术创新中心,石家庄 050024

河北省信息融合与智能控制重点实验室,石家庄 050024

河北鹏博通信设备有限公司,河北沧州 062250

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室内定位 超宽带 锚节点选择 接收信号强度 克拉美罗下界 无监督

2024

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子:1.227
ISSN:1001-0920
年,卷(期):2024.39(12)