Gaussian Process Regression for Fingerprinting based Localization

被引：74

作者：

Kumar, Sudhir ^{[1
]}

Hegde, Rajesh M. ^{[1
]}

Trigoni, Niki ^{[2
]}

机构：

[1] Indian Inst Technol, Dept Elect Engn, Kanpur, Uttar Pradesh, India

[2] Univ Oxford, Dept Comp Sci, Oxford OX1 2JD, England

来源：

AD HOC NETWORKS | 2016年 / 51卷

基金：

英国工程与自然科学研究理事会;

关键词：

Localization; Gaussian process regression; Cramer-Rao bound;

D O I：

10.1016/j.adhoc.2016.07.014

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramer-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：1 / 10

页数：10

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