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中文摘要: 针对如何在锚节点密度较低的情况下提高无线传感器网络中节点自定位精度的问题，本文提出了一种基于RSSI和TDOA组合测距的加权质心定位算法。该算法分别对传统RSSI和TDOA测距模型增加了校验参数及温度补偿,将未知节点与锚节点间距离估计值的倒数作为权值参数，再利用加权质心算法计算出未知节点的位置坐标。硬件试验表明室内环境中基于改进RSSI测距模型的定位算法相比于传统RSSI质心定位算法的误差改进比率为56.2%，仿真结果显示基于组合测距的定位算法在锚节点密度较低时也能达到较高的定位精度。
Abstract:In order to improve the self-localization accuracy at a low beacon node density in Wireless Sensor Networks(WSN).A weighted centroid localization algorithm based on received signal strength indication(RSSI) and time difference of arrival(TDOA) is proposed.The algorithm adds calibration parameters and temperature compensation for RSSI and TDOA ranging model.The inverse of the estimate distance between the unknown node and beacon node is used as the weight parameter.Then the position coordinates of unknown nodes are calculated by the weighted centroid algorithm.The hardware test and software simulation results show that the error improvement rate of proposed algorithm is more than 50% and it can achieve a relatively high localization accuracy under the condition of low beacon node density.
keywords: wireless sensor networks location received signal strength indication(RSSI) time difference of arrive(TDOA)
文章编号：cg15001223 中图分类号： 文献标志码：
基金项目:基于MEMS惯性传感器网络的帕金森病姿态监控和评估技术
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