An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

中文翻译：

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基于核岭回归和特征约简的指纹室内定位方法

室内定位系统的重要目标是提高定位精度并降低功耗。在本文中，我们提出了一种基于接收信号强度（RSS）指纹的室内定位方法。所提出的方法使用某种标准在离线阶段而不是在线阶段中选择固定接入点（FP）进行位置估计。应用主成分分析（PCA）可以减少RSS测量的功能，但保留尽可能多的信息以建立定位模型。然后，基于核的岭回归方法用于获得RSS度量的主要成分与目标位置之间的非线性关系。我们彻底研究了该方法在现实的无线局域网（WLAN）和无线传感器网络（WSN）室内环境中的性能，并与最近开发的方法进行了比较。实验结果表明，与常用的定位方法相比，该方法对参考点的密度依赖性较小，定位精度较高，并且可以适应不同的应用环境。