Zhou Rong, Meng Fengying, Zhou Jing, Teng Jing
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
Sensors (Basel). 2022 Jul 20;22(14):5411. doi: 10.3390/s22145411.
In indoor positioning, signal fluctuation is one of the main factors affecting positioning accuracy. To solve this problem, a new method based on an integration of the empirical mode decomposition threshold smoothing method (EMDT) and improved weighted K nearest neighbor (WKNN), named EMDT-WKNN, is proposed in this paper. First, the nonlinear and non-stationary received signal strength indication (RSSI) sequences are constructed. Secondly, intrinsic mode functions (IMF) selection criteria based on energy analysis method and fluctuation coefficients is proposed. Thirdly, the EMDT method is employed to smooth the RSSI fluctuation. Finally, to further avoid the influence of RSSI fluctuation on the positioning accuracy, the deviated matching points are removed, and more precise combined weights are constructed by combining the geometric distance of the matching points and the Euclidean distance of fingerprints in the positioning method-WKNN. The experimental results show that, on an underground parking dataset, the positioning accuracy based on EMDT-WKNN can reach 1.73 m in the 75th percentile positioning error, which is 27.6% better than 2.39 m of the original RSSI positioning method.
在室内定位中，信号波动是影响定位精度的主要因素之一。为了解决这个问题，本文提出了一种基于经验模态分解阈值平滑方法（EMDT）和改进加权K近邻（WKNN）相结合的新方法，即EMDT-WKNN。首先，构建非线性和非平稳的接收信号强度指示（RSSI）序列。其次，提出基于能量分析方法和波动系数的本征模态函数（IMF）选择准则。第三，采用EMDT方法对RSSI波动进行平滑处理。最后，为了进一步避免RSSI波动对定位精度的影响，去除偏差匹配点，并在定位方法WKNN中结合匹配点的几何距离和指纹的欧几里得距离构建更精确的组合权重。实验结果表明，在一个地下停车场数据集上，基于EMDT-WKNN的定位精度在第75百分位定位误差中可达1.73米，比原始RSSI定位方法的2.39米提高了27.6%。
