采用RSSI模型的无线传感器网络协作定位算法

Cooperativelocalization algorithmapplying RSSI modelin wireless sensor network

由于无线传感器网络定位成本较高,精度不能满足要求以及通信和计算开销过大等问题,提出一种针对定位各阶段实施误差抑制措施的接收信号强度指示(RSSI)测距的协作定位算法。测距阶段通过周期性测量获得模型动态参数,采用相对误差系数对RSSI测距进行校正,定位阶段则基于泰勒级数扩展线性最小二乘方法实现位置估计,采取残差加权法优化位置坐标,减小非视距(NLOS)的不利影响。引入协作定位,将符合要求的节点升级为参考节点参与定位计算,进一步提高定位覆盖率和精度。实验结果表明,所提算法精度接近基于真实坐标的泰勒级数扩展LS算法,相同条件下的精度远高于传统估计算法。节点最大定位误差为0.15,最小定位误差为0.08,网络节点平均定位误差为0.109,能够满足大规模无线传感器网络(WSN)的定位需求。

In view of high cost, low precision, and too large communication and computation overhead in the wireless sensor network localization, a cooperative localization algorithm based on received signal strength indicator （RSSI） ranging for the implementation of error suppression measures in each stage is proposed in this paper. In the ranging stage, the dynamic parameters of the model are obtained by periodic measurement, and the relative error coefficient is used to correct the ranging. In the positioning stage, the positioning estimation is realized by using the linear least square method based on Taylor series expansion, andthe residual weighting method is taken to optimize the position coordinates and reduce the adverse effects of non-line of sight （NLOS）. The concept of collaborative positioningis introduced, which makes the nodes that meet the requirements to upgrade to the reference node, to participate in the positioning of other nodes, which further improves the positioning coverage and positioning accuracy. The experimental results show that the accuracy of the proposed algorithm is close to the Taylor series expansion LS algorithm based on real coordinates. Under the same condition, the positioning accuracy is much higher than that of the traditional position estimation algorithm. The maximum positioning error of the network node is 0.15, the minimum positioning error is 0.08, and the average positioning error is 0. 109, which can meet the needs of large-scale wireless sensor network （WSN） positioning.