基于距离重构的无线传感器网络多维定标定位算法

无线传感器网络中基于多维定标的定位算法通常采用最短路径代替距离矩阵中的未知项，会导致较大的定位误差。针对这一问题，提出一种基于距离矩阵重构的无线传感器网络多维定标定位算法DR-MDS。算法利用节点间的公共邻居信息对距离矩阵线性重构，计算距离矩阵中的未知项，然后对重构的距离矩阵运用双中心化并进行特征分解，从而求得网络坐标。由于算法能够更为准确的获得网络节点之间的空间相对关系，并充分利用其空间相关性计算节点相对坐标，可获得较好的定位效果。仿真结果表明，本文提出的DR-MDS算法与MDS-MAP、ISOMAP相比定位精度更高，误差范围更小。

The Multidimensional Scaling Positioning Algorithm for Wireless Sensor Networks Based on Distance Reconstruction

The multidimensional scaling (MDS) positioning algorithms of wireless sensor networks usually calculate the unknown items of the distance matrix by the shortest path, which may result in large positioning errors. To solve this problem, we propose the multidimensional scaling localization algorithm based on distance reconstruction (DR-MDS). The algorithm uses the common neighbor information between nodes for distance matrix reconstruction, which can effectively calculate the unknown. Then, we calculate the coordinates by making full use of the spatial correlation between all nodes. Simulation results show that the proposed DR-MDS algorithm get higher positioning accuracy and lower error range compared to the MDS-MAP and ISOMAP.