基于新内点测试与Grid-SCAN的铀尾矿库监测定位算法

面向铀尾矿库放射性核素污染WSN监测中，由于监测环境制约，针对该条件下节点密度低、带状分布网络连通性差等问题，为保证一定定位精度，提出一种新的定位算法。新内点测试利用RSSI值与海伦公式判断是否在三角形内外，无需未知节点周围其他节点信息，并采用Grid-SCAN寻找概率模型选取点，使该算法满足低密度条件的定位并减少其计算量。约束范围内计算网格内各点RSSI值的概率，并以最大概率坐标为定位结果。经过仿真，新算法较APIT算法在定位精度上提高了55%~69%。

A localization algorithm for uranium tailings monitoring based on new interior point test and Grid-SCAN

For radionuclide pollution monitoring by WSN in uranium tailings, in order to solve the problems of low node density and poor connectivity in zonal distribution network caused by the restrict of monitoring environment, a new localization algorithm was proposed to ensure the accuracy of localization. For the new interior point test, the RSSI value and Heron's formula were used to judge whether in the triangle or not without the information of other nodes around the unknown node. The Grid-SCAN was applied to search the selection points of probabilistic model, which made the algorithm satisfy the localization under low density condition and reduced its amount of calculation. The probability of RSSI value for each point in the grid were calculated in the constraint range, and the maximum probability coordinates were taken as the localization results. The simulation results showed that the localization accuracy of new algorithm increased by 55%~69% than that of APIT algorithm.