16-08-2012, 02:48 PM
HORUS: A WLAN-BASED INDOOR LOCATION DETERMINATION SYSTEM
A WLAN-BASED INDOOR.pdf (Size: 606.94 KB / Downloads: 47)
Introduction
As ubiquitous computing becomes more popular, the need for context-aware applications
increases. The context of an application refers to the information that is part of
its operating environment. Typically this includes information such as location, activity
of people, and the state of other devices [1]. Algorithms and techniques that allow
an application to be aware of the location of a device on a map of the environment
are a prerequisite for many of these applications. Examples of location-aware applications
[2–6] include location-sensitive content delivery, where tailored information
is sent to the user based on his current location, direction finding, asset tracking, and
emergency notification.
The growing need for location support systems underscores the importance of addressing
location-awareness problem. For example, government initiatives require that
cellular phone providers should develop a way to locate any phone that makes an emergency
call [7]. In outdoor settings, the Global Positioning System (GPS) [9] has been
used in many commercial applications, as in the case of locating automobiles. Despite
the extraordinary advances in GPS technology, many indoor spaces cannot reliably
receive GPS signals. An indoor system must use different sensors, such as
infrared [10, 11], computer vision [12–14], physical contact [15], ultrasonic [16–18],
or radio frequency (RF) [19–42].
The class of RF-based systems that use an underlying wireless data network [21–
42], such as the IEEE 802.11 wireless network [43], to estimate the user location has
gained attention recently, especially for indoor applications. Many mobile devices and
many buildings, both commercial and residential, are already equipped with off-theshelf
IEEE 802.11b wireless Ethernet. Furthermore, most wireless Ethernet devices
already measure signal strength of the received packets as part of their standard operation
and the signal strength varies noticeably as the distance and obstacles between
wireless nodes change. If an accurate localization system could be developed using
only this technology, then many existing systems could be retrofitted in software and
new systems could be deployed using readily available parts.
However, using a wireless network for location determination has the challenge of
dealing with the noisy characteristics of the wireless channel. The IEEE 802.11b standard
uses radio frequencies in the 2.4 GHz band, which is attractive as it is license-free
in most places around the world. The available adapters are based on spread spectrum
radio technology, where the information signal is spread over several frequencies [44],
so interference on a single frequency does not block the signal. The main problem is
that an accurate prediction of the signal strength in every position of the environment is
a very complex and difficult task because the signal propagates in many unpredictable
ways [45]. The received signal is further corrupted by unwanted random effects such
as noise, interference from other sources and interference between channels.
As waves propagate through an environment, the environment scatters the waves
in a variety of different ways. Reflection, absorption, and diffraction occur when
the waves encounter opaque obstacles; refraction occurs when the waves encounter
translucent obstacles. Scattered waves can either decrease or increase the signal
strength at the reception point. Changes in atmospheric conditions like air temperature
can also affect the propagation of waves and the resulting signal strengths. Unfortunately,
2.4 GHz is a resonant frequency of water, so people absorb radio waves in the
GHz frequency band.
Interference occurs when another radio frequency source generates a signal at the
same frequency that is of comparable or higher strength than the transmitted signal, as
measured by the recipient. The interfering device does not need to be a radio based
transmission device [44, 46]. In the 2.4 GHz frequency band, microwave ovens, Blue-
Tooth devices, 2.4 GHz cordless phones and welding equipment can be sources of
interference. Due to reflection, refraction, diffraction, and absorption of radio waves
by structures and people inside a building, the transmitted signal often reaches the
receiver by more than one path, resulting in a phenomenon known as multi-path fading
[47]. The signal components arriving from indirect paths and the direct path, if this
exists, combine and produce a distorted version of the transmitted signal. Multi-path
fading is the main cause of small-scale variations where a small change in the position
of the receiver (order of wavelength) may lead to a significant change in the received
signal strength [44].
These difficulties are particularly acute when operating indoors. Since there is
rarely a line of sight between the transmitter and the receiver, the received signal is
a sum of components that are often caused by some combination of the previously
described phenomena. The received signal varies with respect to time and especially
with respect to the relative position of the receiver and the transmitter.
Moreover, successive signal strength samples from the same access point are highly
correlated. Therefore, a technique that uses multiple samples from the same access
point to enhance the accuracy has to take this high correlation into account.
Relation between signal strength and distance.
Current location determination techniques for the 802.11 wireless networks suffer
from these noisy characteristics, leading to coarse grained accuracy. Figure 1.1 shows
the relation between the signal strength and distance in an area of 12 × 21 square
inches.
As a result of the noisy wireless channel, it is difficult to capture the relation between
the signal strength and distance, in an indoor environment, using a simple analytical
function. Instead, indoor WLAN location determination systems capture the
signature of different access points at selected locations in the area of interest. The
collection of these signatures have been called in literature the Radio Map. Therefore,
radio map-based WLAN location determination systems work in two phases: offline
training phase, in which the radio map is constructed, and online location determination
phase, in which the signal strength samples received from the access points are
used to “search” the radio map to estimate the user location.