Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

Yanfen LE; Hena ZHANG; Weibin SHI; Heng YAO

doi:10.1631/FITEE.2000093

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Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

Regular Papers | Updated：2022-06-06

- Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression
- 基于改进型分簇和核岭回归的RSS室内定位算法
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 6, Pages: 827-838(2021)
- Affiliations：
  
  School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Author bio：
  
  [ "Yanfen LE, E-mail: leyanfen@usst.edu.cn" ]
  Heng YAO, E-mail: hyao@usst.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000093
  CLC： TN92
- Received：03 March 2020，
  
  Revised：2020-12-24，
  
  Published Online：05 February 2021，
  
  Published：2021-06
- Accepted：
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Yanfen LE, Hena ZHANG, Weibin SHI, et al. Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 827-838.
DOI：

Yanfen LE, Hena ZHANG, Weibin SHI, et al. Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 827-838. DOI： 10.1631/FITEE.2000093.

摘要

智能移动设备和无线传感器网络相关技术的融合发展，使得基于位置的服务受到广泛关注。如何利用无线信号在室内复杂环境下实时获得理想的定位精度，成为当前研究热点之一。提出一种基于接收信号强度（RSS

received signal strength）的位置指纹定位算法。该算法分为离线和在线阶段。离线阶段采用一种改进的分簇方法，采用

中心点分簇算法，把物理位置位于簇外边缘的参考点加入簇指纹库，使得参考位置点的RSS信号特性与物理位置结合。在线定位时，基于簇匹配的粗定位与簇内二次精确定位结合。簇内定位采用基于核岭回归的算法，通过核函数实现RSS信号特性与物理位置非线性关系挖掘，同时算法在簇内成员中进行，减小了时间复杂度。通过两个典型室内环境下的定位实验，探究了基于RSS信号强度和覆盖向量的两种分簇和簇匹配准则对算法性能的影响，以及不同环境下参数的选择，验证了所提算法的定位性能。

Abstract

We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps

an offline phase at which an advanced clustering (AC) strategy is used

an online phase of approximate localization at which cluster matching is used

and an online phase of precise localization with kernel ridge regression. Specifically

after offline fingerprint collection and similarity measurement

we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization

RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then

a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments

and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

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references

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