基于负约束条件下最大似然估计的无线传感网络定位算法

针对基于RSSI(Received Signal Strength Indicator)的无线传感网络定位算法精度不高的问题,提出一种负约束条件下的似然估计定位算法。当未知节点在参考节点的通信范围之外时,引入负约束条件来提高定位精度。主要工作可分为三部分:第一,根据RSSI值测量参考节点与未知节点之间的距离。第二,根据参考节点与未知节点通信关系建立正约束和负约束条件下的似然估计函数。第三,利用粒子群优化算法找到未知节点的最佳位置。仿真结果表明,引入负约束条件可以提高定位精度,且优于传统的定位算法。

Localization in WSN Based on Maximum Likelihood Estimation with Negative Constraint

In order to improve the accuracy of localization in wireless sensor network based on RSSI, we propose a maximum likelihood estimation approach with negative constraints to realize the localization of the unknown nodes. If there is no communication link between anchor node and unknown node, the negative constraints can be employed to improve the localization accuracy. The main work can be divided into three parts: firstly, we measure the distance based on RSSI from the nodes. Secondly, a series of positive and negative constrains are combined to build the modeling using the maximum likelihood estimation. Finally, particle swarm optimization is employed to find the optimal position. The simulation results show that the proposed approach outperforms some existing localization algorithm without negative constrains.