摘要
传统的基于接收信号强度的定位算法均假设用于线下训练和实时定位的移动终端不变,而这会严重影响基于位置指纹定位法的准确性。本文提出的接收信号强度差值法(RSSD)和实时自适应学习规范化法(RSALS),用于解决不同WLAN移动终端获取接收信号强度存在差异的问题,并在真实室内WLAN环境下验证了算法的可行性和有效性。实验表明即使在设备不变的情况下RSALS法仍然具有实时校正的作用,可以在一定程度上抵消环境变化对定位精度的影响。
Traditional positioning algorithms based on RSS reckon on the assumption that the mobile terminals used for off-line training and real-time positioning behave identically;this leads to significantly low localization accuracy. The objective of this paper is to investigate the positioning methods for variance in RSS with different WLAN capa-ble mobile devices. Two positioning algorithms are considered:RSSD ( RSS Difference) and RSALS( Real-time Self Adaptive Learning Standardization) . And also, this paper presents an experiment made in a real indoor WLAN en-vironment and the results and their analysis verify the feasibility and validity of the proposed algorithms. The experi-mental results and their analysis indicate preliminarily that RSALS and RSSD are still effective without mobile de-vice diversity; the results can be explained as being due to partial offset of the positioning accuracy impact of the environmental change.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2014年第3期481-485,共5页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(50505039)资助
关键词
算法
天线
实验
最小二乘法
线性回归
MATLAB
最大似然估计
无线局域网
位置指纹
接收信号强度
接收信号强度差值法
实时自适应学习法
algorithms, antennas, experiments, leastmum likelihood estimation, wireless localceived Signal Strength), RSS Difference,squares approximat!ons, linear regression, MATLAB, maxi- area networks ( WLAN )
location fingerprinting, RSS ( Re- RSALS ( Real-time Self Adaptive Learning Standardization)