特征缩放k-近邻算法提升室内定位精度

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"这篇研究论文探讨了一种基于特征缩放的k最近邻（FS-kNN）算法，用于改进室内定位系统的精度。随着WLAN基础设施的普及，基于WiFi信号指纹的室内定位系统已成为研究热点。现有的方法主要采用分类技术，通过匹配移动站（MS）报告的射频信号强度（RSS）向量与预先训练的参考指纹，这些参考指纹来自不同接入点（APs）在已知位置的不同参考点（RPs）。然而，在计算信号距离时，现有技术未考虑同一RSS级别上的等差可能并不意味着复杂室内环境中几何距离的等差。为解决这一问题，论文提出了一种新的方法，即FS-kNN算法，它构建了一个RSS级别相关的特征缩放模型，以更好地适应室内环境中的信号变化并提高定位准确性。" 文章深入分析了室内定位系统中k近邻算法存在的问题，即传统的k-NN算法在处理RSS差异时忽视了室内环境中RSS值的不均匀性。在复杂的室内环境中，相同的RSS差异并不一定对应于实际距离的相同变化。因此，作者提出了特征缩放的概念，将RSS值的尺度调整到一个统一的标准，使得更小的RSS变化在定位中能够得到更准确的权重。 FS-kNN算法的核心步骤包括以下几个方面： 1. **特征缩放**：首先，对每个AP的RSS数据进行标准化处理，使所有RSS值在同一尺度上，这样可以消除不同信号强度级别上的不平等权重。 2. **距离度量**：使用标准化后的RSS值计算与参考指纹之间的距离，这使得小的信号强度差异在计算中占据更重要的角色。 3. **k-NN决策**：通过考虑特征缩放后最接近的k个邻居，确定目标位置。由于考虑了RSS的相对变化，FS-kNN算法能提供更精确的定位估计。此外，论文可能还包括了算法的实现细节、性能评估以及与其他定位算法的比较。实验部分可能展示了在各种室内环境和不同条件下的定位误差，证明了FS-kNN算法相比传统方法在提高定位精度方面的优势。该研究为室内定位领域提供了一种改进的算法，通过特征缩放优化了k-NN方法，从而在处理室内信号的复杂性和不一致性时提高了定位的准确性和可靠性。这一贡献对于依赖室内定位服务的应用，如智能建筑、物联网设备和紧急响应系统，具有重要的实践意义。

590 IEEE INTERNET OF THINGS JOURNAL, VOL. 3, NO. 4, AUGUST 2016

A Feature-Scaling-Based k-Nearest Neighbor

Algorithm for Indoor Positioning Systems

Dong Li, Baoxian Zhang, Senior Member, IEEE,andChengLi,Senior Member, IEEE

Abstract—With the increasing popularity of WLAN infras-

tructure, WiFi ﬁngerprint-based indoor positioning systems have

received considerable attention recently. Much existing work in

this aspect adopts classiﬁcation techniques that match a vector

of radio signal strengths (RSSs) reported by a mobile station

(MS) to pretrained reference ﬁngerprints sampled from differ-

ent access points ( APs) at different reference points (RPs) with

known positions. However, in the calculation of signal distances

between different RSS vectors, existing techniques fail to consider

the fact that equal RSS differences at different RSS levels may not

mean equal differences in geometrical distances in complex indoor

environment. To address this issue, in this paper, we propose

a feature-scaling-based k-nearest neighbor (FS-kNN) algorithm

for achieving improved localization accuracy. In FS-kNN, we

build a novel RSS-level-based FS model, which introduces RSS-

level-based scaling weights in the computation of effective signal

distances between signal vector reported by a MS and reference

ﬁngerprints in a radio map. Experimental results show that FS-

kNN can achieve an average location error as low as 1.70 m, which

is superior to existing work.

Index Terms—Feature scaling (FS), ﬁngerprint-based

localization, indoor positioning system, k-nearest neighbor

(kNN).

I. INTRODUCTION

NDOOR positioning has received great attention recently

because position information is essential for providing

location-based services (LBSs) [1], which offers intelligent ser-

vices in various ﬁelds in the context of Internet of Things (IoT)

[2]–[4]. For example, position-based navigation has been used

inside Copenhagen Airport [5]. Passengers there can use it to

plan their paths inside the airport and to get expected informa-

tion in an interactive way. Moreover, an indoor tracking system

was deployed in Hartford hospital, which helps tracking expen-

sive equipment and also assisting patients there to efﬁciently

Manuscript received August 07, 2015; accepted October 06, 2015. Date

of publication October 27, 2015; date of current version July 27, 2016. This

work was supported in part by the National Science Foundation (NSF) of

China under Grant 61531006, Grant 61471339, and Grant 61173158, in part

by the National Science and Technology R&D Program of China under Grant

2014BAK06B01, and in part by the Natural Sciences and Engineering Research

Council (NSERC) of Canada under Discovery Grant 293264-12 and Strategic

Project Grant STPGP 397491-10. An earlier version of this paper was presented

at the IEEE GLOBECOM 2014—Ad Hoc and Sensor Networks Symposium,

Austin, TX, USA, Dec. 8–12, 2014.

D. Li is with the Research Center of Ubiquitous Sensor Networks,

University of Chinese Academy of Sciences, Beijing 100049, China (e-mail:

lidong10b@mails.ucas.ac.cn).

B. Zhang is with the Research Center of Ubiquitous Sensor Networks,

University of Chinese Academy of Sciences, Beijing 100049, China, and also

with the Jiangsu Internet-of-Things Research and Development Center, Wuxi

214135, China (e-mail: bxzhang@ucas.ac.cn).

C. Li is with the Faculty of Engineering and Applied Science, Memorial

University, St. John’s, NL A1B 3X5, Canada (e-mail: licheng@mun.ca).

Digital Object Identiﬁer 10.1109/JIOT.2015.2495229

use medical resources in the hospital [6]. In addition, location-

aware advertising usually delivers location-speciﬁc coupons or

discount information to customers based on their locations and

interests [7]. However, indoor environments are very compli-

cated such that there usually exist many obstacles, such as

walls, furniture, human beings, and consequently ﬂuctuations

of wireless signals because of multipath effects. These obsta-

cles and the signal ﬂuctuations at different scales can cause

signiﬁcant degradation in the accuracy of indoor positioning,

which limits the usefulness and degree of comfortableness for

providing practical LBS services.

One popular way to estimate a mobile user’s position in

indoor environment is to use reference ﬁngerprints based on

WiFi signals (e.g., RADAR [8]). In general, there are two

phases for a ﬁngerprint-based positioning algorithm: 1) ofﬂine

phase and 2) online phase. During ofﬂine phase, a target envi-

ronment is calibrated by performing a site-survey with a mobile

station (MS). The whole area is typically overlaid with a set

of predetermined grid points known as reference points (RPs)

where sample data should be acquired. At each RP, many sam-

ples of radio signal strengths (RSSs) from different access

points (APs) are repeatedly collected. Each RP has a position

known apriori. Then, a vector of mean RSS values associated

with the position of the RP is recorded into a database known

as a radio map. During online phase, an MS at unknown posi-

tion reports its instantly sampled vector of RSS values from

surrounding APs to a remote server. The server uses a pattern-

matching algorithm to estimate the current position of the MS

and then returns the estimated position back to the MS. A com-

monly used pattern-matching algorithm to estimate the position

is the k-nearest neighbor (kNN) algorithm, which ﬁnds the k

ﬁngerprints with the minimum signal distances to the instantly

reported RSS vector among different ﬁngerprints in the radio

map that the server locally stores. The ﬁrst k RPs resulting in

the minimum signal distances will be used to derive the esti-

mated location. One big issue in kNN and also its variants (e.g.,

[9], [10]) is that, i n their calculation of signal distance, equal

RSS distances are always equally treated. That is to say, equal

RSS differences are assumed to account for equal geometrical

distances. However, this may not be true in indoor environment.

In reality, equal RSS differences at different RSS levels may

often be caused by different geometrical distances, especially,

in complex indoor environment. The performance of an indoor

localization algorithm can be largely degraded if the impacts of

RSS differences at different levels on positioning accuracy are

not fully considered.

To address the above issue, in this paper, we ﬁrst conduct

two motivating experiments, which demonstrate that equal RSS

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