KPCA-ESN方法在Wi-Fi室内定位中的应用

Application of KPCA-ESN Method in Wi-Fi Based Indoor Positioning

针对动态的室内环境及时变的接收信号强度(Received Signal Strength,RSS)值对定位精度的影响,提出一种基于核主成分分析(Kernel Principal Component Analysis,KPCA)和回声状态网络(Echo State Networks,ESN)相结合的Wi-Fi室内定位方法。KPCA方法对RSS指纹信息进行预处理,有效提取模型输入的非线性主元。利用ESN方法构建所提取出的定位特征与物理位置之间的非线性映射关系。将所提出的KPCA-ESN方法应用于仿真与物理环境的Wi-Fi室内定位实例中,在同等条件下,还与其他定位方法进行比较。结果表明,该方法定位精度较高,能够适应动态环境变化。

Aiming at the problem that the positioning accuracy is affected by the dynamic indoor environment and time-varying received signal strength （RSS） values, a Wi-Fi based indoor positioning method using kernel principal component Analysis （KPCA） and echo state networks （ESN） is proposed. The KPCA method is used to preprocess the RSS fingerprints effectively and extract the nonlinear principal components of the inputs of the model On the basis of KPCA, the extracted principal components are taken as the inputs to the ESN network, the nonlinear mapping between corresponding positioning features and physical locations is then established by the ESN. The proposed KPCA-ESN method is then applied to Wi-Fi based indoor positioning instances by simulation and physical environment experiments. Compared with the other positioning methods under the same condition, experimental results confirm that the proposed method has higher positioning accuracy, and can also automatically timely adapt to environmental dynamics.