honeycomb wireless localization 蜂窝无线定位
Learning-Based Localization in Wireless 基于学习算法的无线传感器网络定位问题研究
A key obstacle to high accuracy location is the Non-Line-of-Sigh(tNLOS) transmission of signal in wireless localization. It adds positive error to Time of Arriva(lTOA) measures.
在无线定位中,精确定位面临的一个主要问题是信号的非视距传播(NLOS),NLOS的传播会给距离测量值增加较大的正性误差。
参考来源 - 距离几何TOA无线定位算法In order to improve indoor wireless localization, and reduce complexcity, this paper presents an improved indoor wireless localization algorithm. It includes two algorithms.
为提高室内无线定位精度并降低算法复杂度，提出一种改进的室内无线定位算法，包含2种分支算法。
参考来源 - 一种改进的室内无线定位算法·2,447,543篇论文数据，部分数据来源于NoteExpress
Applying the classical graph drawing algorithms to node localization in wireless sensor networks is a novel idea.
将经典的画图算法应用到无线传感器网络节点定位问题是一个全新的思路。
Discuss the Sequential Monte Carlo localization method for wireless sensor networks scheme and modify the basic algorithm to overcome the sample degeneracy problem in resampling stage.
讨论了贯序蒙特卡罗方法在无线传感器网络节点定位算法中的实现，并针对再采样阶段的样本缺失现象，对基本算法进行了改进。
The node localization technology in wireless sensor networks was studied, and a flexible trilateration localization(FTL) based on RSSI was presented.
研究了无线传感器节点定位问题，在三边测量法定位基础上提出了一种基于RSSI的灵活的节点定位机制（FTL）。
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