CAJ | 학술논문

在森林防火、目标追踪、灾难预警、环境监测等应用中,需要通过定位算法对无线传感器节点进行三维定位.提出一种基于多维定标的无线传感器网络三维定位算法,结合RSS经验衰减模型和最短路径建立相异性矩阵,采用轻量级矩阵分解算法降低相异性矩阵分解的计算复杂性,并利用网络中存在的周期性消息将初始定位信息回送,在后台使用迭代优化算法对初始定位结果求精.仿真实验表明,在测距误差一定的情况下,该算法能够提高节点三维坐标的初始计算精度,经过集中式的优化求精后与MDS-MAP算法相比,能够明显地提高节点三维定位的精度.
재삼림방화、목표추종、재난예경、배경감측등응용중,수요통과정위산법대무선전감기절점진행삼유정위.제출일충기우다유정표적무선전감기망락삼유정위산법,결합RSS경험쇠감모형화최단로경건립상이성구진,채용경량급구진분해산법강저상이성구진분해적계산복잡성,병이용망락중존재적주기성소식장초시정위신식회송,재후태사용질대우화산법대초시정위결과구정.방진실험표명,재측거오차일정적정황하,해산법능구제고절점삼유좌표적초시계산정도,경과집중식적우화구정후여MDS-MAP산법상비,능구명현지제고절점삼유정위적정도.
In wireless sensor networks, three-dimensional 3D localization is very crucial for the applications of fire preventing, target tracking, disaster warning, and environmental monitoring, etc. This paper proposed a three-dimensional localization algorithm based on multidimensional scaling. Established the dissimilarity matrix by combining the experience attenuation model of RSS and the shortest path method. Reduced the computation complexity of decomposing dissimilarity matrix by a lightweight matrix decomposition algorithm. At the back end, refined the initial positioning coordinates by the iterative optimization algorithm. Simulation results show that the algorithm, in the case of stable measurement error, can refine the initial coordinates and achieve a higher precision of localization in three-dimensional space compared with the MDS-MAP algorithm.