Regional Propagation Model Based Fingerprinting Localization in Indoor Environments
Regional Propagation Model Based Fingerprinting Localization in Indoor Environments
Regional Propagation Model Based Fingerprinting Localization in Indoor Environments
Institute of Broadband Wireless Mobile Communications, Beijing Jiaotong University, Beijing 100044, China
State Key Laboratory of Rail Trafc Control and Safety, Beijing Jiaotong University, Beijing 100044, China
I. I NTRODUCTION
In recent years, the increasing demands of location based
services have promoted the development of indoor positioning
techniques. Some applications and services, such as navigation
and advertising in large-scale shopping mall, require high
precision and real-time localization performance to send the
location related information to the end-users. The traditional
localization method is triangulation, which estimates the distances by measuring the received signal strength (RSS), time
of arrival (TOA) and time difference of arrival (TDOA) of
indoor wireless LAN (WLAN) signals transmitted by the
access points (AP) and received at the mobile terminals or
vice versa. However, the accuracy of triangulation is affected
by factors including non-line-of-sight propagation, multipath
effect and specic site parameters (e.g. indoor layout, moving
objects).
One of the preferable location algorithms for indoor environments is the RSS based ngerprinting algorithm [1]. The
method falls into two phases, the ofine phase and the online
phase. In the former phase, a ngerprinting map (FM) is generated by collecting a set of RSS values from various APs at
the predened reference points (RPs). In the second phase, the
location estimation of the terminal is carried out by matching
online measurements with the closest ngerprints in the FM.
The current matching algorithms in ngerprinting localization
include K-nearest neighbor (KNN), weighted KNN (WKNN)
[2], kernel based method [3], support vector machines (SVM)
[4], and articial neural networks (ANN) [5]. As shown in the
literatures [25], ngerprinting localization could obtain mean
291
Fig. 1.
Testing scenarios.
(2)
292
35
4
wi
w i li
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75
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4
(8)
i=1
1
where wi = +d(
r,ri ) , = 0.01 is a small real constant used
to avoid division by zero, and the value of k is 4 in our tests.
40
45
5
10
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2
5
(6)
i=1
15
(5)
k
10
45
5
l = 1
k
40
j {1, 2, ..., m}
30
th
25
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15
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293
0.9
1
RPM
OSM
DSM
COST231MWM
0.7
0.6
0.9
0.5
0.4
0.3
0.2
0.1
0
Fig. 3.
S3.
10
RSS Prediction Error (dB)
15
RFMRPM(S1)
RFMRPM(S2)
RFMRPM(S3)
SFM(S1)
SFM(S2)
SFM(S3)
0.8
Prob.error < Abscissa
0.8
0.7
0.6
0.5
0.4
0.3
0.2
20
0.1
0
Fig. 5.
4
6
Location Estimation Error (m)
10
0.7
3
0.6
0.5
2.8
0.4
2.6
0.3
RFMRPM(S3)
RFMOSM(S3)
RFMDSM(S3)
RFMCOST231MWM(S3)
SFM(S3)
0.2
0.1
0
0.8
3
4
5
Location Estimation Error(m)
RFMRPM (S3)
RFMOSM (S3)
RFMDSM (S3)
RFMCOST231MWM (S3)
SFM (S3)
2.4
2.2
2
1.8
1.6
1.4
7
8
9
Access Point Number
10
11
12
Fig. 6. Effects of the number of selected APs on the mean location error
when sparse degree is S3.
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[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
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