基于接收信号强度指示测距的蒙特卡罗盒移动节点定位算法

针对无线传感器网络(WSN)中以蒙特卡罗为基础的移动节点定位算法在采样效率和定位精度方面的不足,提出一种基于接收信号强度指示(RSSI)测距的蒙特卡罗盒定位(MCB)算法。通过对RSSI测距信息分区间管理来加强过滤条件,提高定位精度;同时采样阶段利用已满足过滤条件的样本点生成更有效的样本,从而提高采样效率;最后通过牛顿插值法预测节点运动轨迹,样本点与未知节点运动轨迹越接近则其权值越大,据此对样本点进行加权处理得到节点的最佳估计位置。仿真结果表明,改进方案在不同的锚节点密度、通信半径、运动速度等情况下均表现出良好性能,且定位精度与同等条件下的蒙特卡罗盒算法相比均有提高。

Monte Carlo boxed localization algorithm for mobile nodes based on received signal strength indication ranging

To solve the shortcomings of sampling efficiency and positioning accuracy of the Monte Carlo localization algorithm in Wireless Sensor Networks (WSN), a Monte Carlo localization Boxed (MCB) algorithm for mobile nodes based on Received Signal Strength Indication (RSSI) ranging was proposed. To improve the positioning accuracy, the filter conditions was strengthened by mapping the ranging information into different distance intervals. At the same time, the samples which had already met the filter conditions were used to create more effective samples so as to improve the sampling efficiency. Finally, the Newton interpolation was used to predict the nodes' trajectory. The closer the trajectory between the sample and the node is, the greater the weight of the sample is, and the best estimate position could be obtained with these weighted samples. The simulation results indicate that the proposed algorithm has good performance in different density of anchor node, communication radius, and movement velocity etc., and compared with the MCB algorithm, the proposed algorithm has higher positioning accuracy.