indoor geo location system

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Indoor Geo Location System

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CMRIT, Department of ECE

1. INTRODUCTIONRecently, there is an increasing interest in accurate location finding techniques and

location based applications for indoor areas. The Global Positioning System (GPS) and

wireless enhanced 911(E-911) services also address the issue of location finding. However,

these technologies cannot provide accurate indoor geo location, which has its own

independent market and unique technical challenges.

Despite extraordinary advances in global positioning system (GPS) technology, millions

of square meters of indoor space are out of reach of GPS satellites. Their signals, originating

high above the earth, are not designed to penetrate most construction materials, and no

amount of technical wizardry is likely to help. So the greater part of the world's commerce,

being conducted indoors, cannot be followed by GPS satellites.

1.1.What is indoor geo location?

Indoor Geo location is the process of findinggeo locationof an electronic device inside

a building. Since technologies used for indoor geo location are substantially different from

technologies used for outdoorgeo locationthis area is considered as a separate area of

science and technology.

1.2. Need for indoor geo localization:

Consider some everyday business challenges. Perpetual physical inventory is needed

for manufacturing control, as well as to keep assets from being lost or pilfered. Mobile assets,

such as hospital crash carts, need to be on hand in an emergency. Costly and baroqueprocedures presently track and find manufacturing work-in-process. Nor is the office

immune: loss of valuable equipment such as laptop computers has become a serious problem,

and locating people in a large office takes time and disrupts other activities.

1.3. Tracking:

What these systems share is a need to find and track physical assets and people that

are inside buildings. The design differences between an efficient asset-tracking system and

GPS arc more basic. First and foremost, control of the situation shifts from users of GPS

receivers, querying the system for a fix on their position, to overhead scanners, checking upon the positions of many specially tagged objects and people. In GPS, each receiver must

determine its own position in reference to a fixed infrastructure, whereas inside a building,

the tracking infrastructure must keep tabs on thousands of tags.
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2. ARCHITECTURE OF INDOOR GEOLOCATION SYSYTEMThe architecture mainly consists of 3 parts.

1. Geolocation base station (GBS)

2. Geolocation control station (GCS)

3. Mobile station (MS)

Similar to the cellular geo location system, the architecture of indoor geo location

system is mainly classified into 2 categories. Mobile based architecture and network based

architecture. Most of the indoor geo location techniques proposed to date have been focused

on network based system architecture. The geo location base station(GBS) extract location

metrics from the radio signals transmitted by the mobile station and relay the information to a

geo location control station(GCS). The communication between GBS and GCS can be either

wired or wireless. Then the position of the mobile station is estimated, displayed and tracked

at the GCS. With the mobile based system architecture the mobile station estimates self

position by measuring received radio signals from multiple fixed GBS. Compared to mobile

based architecture, network based architecture system has the advantage that mobile station

can be implemented as a simple structured transceiver with small size and low power

consumption that can be easily carried by people or attached to valuable equipments like tag.


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3. TYPES OF VARIOUS INDOOR GEOLOCATION TECHNIQUESIt is not easy to model the radio propagation in the indoor environment because of

severe multipath, low probability for availability of line-of-sight (LOS)path, and specific site

parameters such as floor layout, moving objects, and numerous reflecting surfaces. There is

no good model for indoor radio multipath characteristic so far. Except using traditional

triangulation, positioning algorithms using scene analysis or proximity are developed to

mitigate the measurement errors. Targeting different applications or services, these three

algorithms have unique advantages and disadvantages. Hence, using more than one type of

positioning algorithms at the same time could get better performance.

Fig.1.Types of various indoor geo location systems

3.1. Direction-Based technique:The direction-based technique consists of the Angle of Arrival Method (AOA) which uses

the Ling of Sight (LOS) approach.

GEOLOCATION

TECHNIQUES

DIRECTION -

BASED

DISTANCE -

BASED

ARRIVAL TIME

METHOD

SIGNAL

STRENGTH

METHOD

RECEIVED SIGNAL

PHASE METHOD

FINGERPRINTING -

BASED


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3.1.1.Angle of arrival:The angle of arrival geo location technique the direction of arrival of the received

signal to determine the location of the mobile station. The direction of the incoming signals

are received and measured from the target transmitter to a direction using directional

antennas or antenna arrays. If the line of sight signal path is blocked, then the angle of arrival

being used is a reflected or scattered signal for its direction estimation. This technique is not

feasible when dealing with indoor geo location because often times there will be walls or

objects blocking the line of sight signal path.

Direction is determined from the AOA (angle of arrival) of received signal. Receiver

(GBS) measures the direction of the received signal from the target transmitter (MS) using

directional antennas / antenna array. Accuracy depends on where the transmitter is located

with respect to the receiver. More than 2 receivers are needed for accuracy.

In AOA, the location of the desired target can be found by the intersection of several

pairs of angle direction lines, each formed by the circular radius from a base station or a

beacon station to the mobile target.AOA methods may use at least two known reference

points (A, B), and two measured angles 1, 2 to derive the 2-D location of the target P.

Estimation of AOA, commonly referred to as direction finding (DF), can be accomplished

either with directional antennae or with an array of antenna.


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3.1.1.1. Advantages:

A position estimate may be determined with as few as three measuring units for 3-D

positioning or two measuring units for 2-D positioning, and that no time synchronization

between measuring units is required.

3.1.1.2. Disadvantages:

The disadvantages include relatively large and complex hardware requirement(s), and

location estimate degradation as the mobile target moves farther from the measuring units.

For accurate positioning, the angle measurements need to be accurate, but the high

accuracy measurements in wireless networks may be limited by shadowing, by multipath

reflections arriving from misleading directions, or by the directivity of the measuring

aperture.

Some literatures also call AOA as direction of arrival (DOA.).

3.1.1.3. Application:

Can be used in next generation cellular system where smart antennas are used to

increase capacity.

3.2. Distance Based Technique:The Distance-Based Techniques involve the following methods:-

-Time of Arrival (TOA)

-Time Difference of Arrival (TDOA)

-Signal Strength

-Received Signal Phase

3.2.1.Time of Arrival:The time of arrival technique uses the distance between the mobile station and

receiver to estimate location.At least three measurements are necessary to calculate the

possible position of the user. The measurements estimate the position in two dimensions and

four measurements estimate the position in three dimension. By estimating the distance

between the receiver and the mobile to be d, the mobile is located on a circle with radius d

centered on the receiver. Distance is equal to the velocity of light multiplied by the time taken

by the signal to reach the base station.

The distance from the mobile target to the measuring unit is directly proportional to

the propagation time. In order to enable 2-D positioning, TOA measurements must be made

with respect to signals from at least three reference points, as shown in Fig. For TOA-basedsystems, the one-way propagation time is measured, and the distance between measuring unit


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and signal transmitter is calculated. In general, direct TOA results in two problems. First, all

transmitters and receivers in the system have to be precisely synchronized. Second, a

timestamp must be labeled in the transmitting signal in order for the measuring unit to discern

the distance the signal has traveled. TOA can be measured using different signaling

techniques such as direct sequence spread-spectrum (DSSS) or ultrawide band (UWB)measurements.

A straightforward approach uses a geometric method to compute the intersection

points of the circles of TOA. The position of the target can also be computed by minimizing

the sum of squares of a nonlinear cost function, i.e., least-squares algorithm. It assumes that

the mobile terminal, located at (x0, y0), transmits a signal at time t0, the N base stations

located at (x1, y1), (x2, y2), . . . ,(xN , yN ) receive the signal at time t1, t2, . . . , tN . As a

performance measure, the cost function can be formed by

fi(x) is given as follows.

fi(x) = c(ti t) sqr root((xi x)^2+ (yi y)^2)

where c is the speed of light, and x = (x, y, t)T.

This function is formed for each measuring unit, i = 1, . . . , N , and fi(x) could

be made zero with the proper choice of x, y, and t. The location estimate is determined by

minimizing the function F(x).

Fig.3.Position Based on TOA method


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There are other algorithms for TOA-based indoor location system such as closest-

neighbor (CN) and residual weighting (RWGH). The CN algorithm estimates the location of

the user as the location of the base station or reference point that is located closest to that user.

The RWGH algorithm can be basically viewed as a form of weighted least-squares algorithm.

It is suitable for LOS, non-LOS (NLOS) and mixed LOS/NLOS channel conditions.

3.2.2.Time difference of arrival:Some outdoor geo location applications use the time difference of arrival technique.

This is where the differences of the time of arrivals are use to locate the mobile. The time

difference of arrival technique is similar to that of the time of arrival technique but instead of

using circles, the time difference of arrival uses hyperbolas on which the transmitter must be

located with foci at the receiver. At least three measurements are needed to calculate a fixed

position at the intersection of the hyperbolas.

The idea of TDOA is to determine the relative position of the mobile transmitter by

examining the difference in time at which the signal arrives at multiple measuring units,

rather than the absolute arrival time of TOA. For each TDOA measurement, the transmitter

must lie on a hyperboloid with a constant range difference between the two measuring units.

Fig.4.Positioning based on TDOA measurement

Except the exact solutions to the hyperbolic TDOA equation shown in through

nonlinear regression, an easier solution is to linearize the equations through the use of a

Taylor-series expansion and create an iterative algorithm. A 2-D target location can be

estimated from the two intersections of two or more TDOA measurements. Two hyperbolas

are formed from TDOA measurements at three fixed measuring units (A, B, and C) to

provide an intersection point, which locates the target P. The conventional methods for

computing TDOA estimates are to use correlation techniques. TDOA can be estimated from


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the cross correlation between the signals received at a pair of measuring units. Suppose that

for the transmitted signal s(t), the received signal at measuring unit i is xi(t). Assume that

xi(t)is corrupted by the noise ni(t) and delayed by di, then

xi(t) =s(t di) + ni(t).

Similarly, the signal xj(t) = s(t dj)+nj(t), which arrives at measuring unit j, is

delayed by dj and corrupted by the noise nj(t).

The TDOA estimate is the value that maximizes Rxi, xj( ), i.e., the range differences. This

approach requires that the measuring units share a precise time reference and reference

signals, but does not impose any requirement on the mobile target. Frequency domain

processing techniques are usually used to calculate . Except the previous TDOA methods, a

delay measurement-based TDOA measuring method was proposed in for 802. 11 wireless

LANs, which eliminates the requirement of initial synchronization in the conventional

method.

3.2.2.1. RLS Algorithm:

It is used when there are errors in the distance measurements. Let the distance di from the i th GBS ,

di=c*t

where c velocity of light

t time taken by the signal to reach the GBS.

Location ofi th GBS(xi , yi) Location of mobile(x , y) We have N equations of the form:

fi(x , y)= (x-xi)^2 + (y-yi)^2-di^2 for i= 1,2,3,,N

.

3.2.3.Signal Strength Method:The signal strength technique uses the transmitted power at the mobile station and the

received signal strength at the base station to provide an estimate of the distance between the

transmitter and the receiver. Similar to the time of arrival technique, the distance gives the

circle centered on the receiver which the mobile transmitter is on.


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Fig.5.Received Signal Strength Method

The above two schemes have some drawbacks. For indoor environments, it is difficult

to find a LOS channel between the transmitter and the receiver. Radio propagation in such

environments would suffer from multipath effect. The time and angle of an arrival signal

would be affected by the multipath effect; thus, the accuracy of estimated location could be

decreased. An alternative approach is to estimate the distance of the mobile unit from some

set of measuring units, using the attenuation of emitted signal strength. Signal attenuation-

based methods attempt to calculate the signal path loss due to propagation. Theoretical and

empirical models are used to translate the difference between the transmitted signal strength

and the received signal strength into a range estimate, as shown in Fig.5.

Due to severe multipath fading and shadowing present in the indoor environment,

path-loss models do not always hold. The parameters employed in these models are site-

specific. The accuracy of this method can be improved by utilizing the premeasured RSS

contours centered at the receiver [7] or multiple measurements at several base stations. A

fuzzy logic algorithm shown in [8] is able to significantly improve the location accuracyusing RSS measurement.


Low complexity receiver.


Very unreliable due to shadow fading (fluctuations around the mean value and expected

value caused due to signal being blocked from GBS).

GBS do not distinguish between signal strength in los path and reflected path


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3.2.4. Received Signal Phase Method:

The received signal phase technique used with reference receivers measures the

carrier phase. Differential Global Positioning System improves location accuracy within

twenty meters to one meter as compared with Global Positioning Systems which uses range

measurements. In indoor geo location systems, it is possible to use signal phase methods with

time of arrival, time difference of arrival or received signal strength techniques to better

estimate the location.

The received signal phase method uses the carrier phase (or phase difference) to

estimate the range. This method is also called phase of arrival(POA). Assuming that all

transmitting stations emit pure sinusoidal signals that are of the same frequency f, with zero

phase offset, in order to determine the phases of signals received at a target point, the signal

transmitted from each transmitter to the receiver needs a finite transit delay. In Fig., the

transmitter stations A up to D are placed at particular locations within an imaginary cubicbuilding. The delay is expressed as a fraction of the signals wavelength, and is denoted with

the symbol i = (2fDi)/c in equation Si(t) = sin(2ft + i),

where i (A, B, C, D), and c is the speed of light. As long as the transmitted signals

wavelength is longer than the diagonal of the cubic building, i.e., 0 < i < 2, we can get the

range estimation Di = (ci)/(2f). Then, we can use the same positioning algorithms using

TOA measurement. The receiver may measure phase differences between two signals

transmitted by pairs of stations, and positioning systems are able to adopt the algorithms

using TDOA measurement to locate the target.

For an indoor positioning system, it is possible to use the signal phase method

together with TOA/TDOA or RSS method to fine-tune the location positioning. However, the

received signal phase method has one problem of ambiguous carrier phase measurements to

overcome. It needs an LOS signal path, otherwise it will cause more errors for the indoor

environment.


Differential GPS can improve location accuracy from about 20m to within 1m.


Ambiguity resulting from periodic property of the signal phase. Multipath condition causes more errors.

3.2.4.3. Application:

Used in indoor geo location system, along with TOA/TDOA or RSS method to fine-tune the location of MS.


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3.3. Finger-print Based Technique:

Another technique that estimates position location is signal fingerprinting. The

multipath structure of the channel is unique to every location and may be considered

fingerprint or signature of the location if the same radio frequency signal is transmitted from

that location. This technique may be used in indoor geo location applications where a

location pattern develops from multipath rays in a multipath structure in an area. Develops a

signature database of a location grid in specific service areas. The received signal is

measured as a vehicle moves along this grid in specific areas and recorded in signature

database. When another vehicle moves in the same area, the signal received from it is

compared with the entry in the database, thus its location is determined.

RF-based scene analysis refers to the type of algorithms that first collect feature

(fingerprints) of a scene and then estimate the location of an object by matching online

measurements with the closest a priori location fingerprints. RSS-based locationfingerprinting is commonly used in scene analysis. Location fingerprinting refers to

techniques that match the fingerprint of some characteristic of a signal that is location

dependent. There are two stages for location fingerprinting: offline stage and online stage (or

run-time stage). During the offline stage, a site survey is performed in an environment. The

location coordinates/labels and respective signal strengths from nearby base

stations/measuring units are collected. During the online stage, a location positioning

technique uses the currently observed signal strengths and previously collected information to

figure out an estimated location. The main challenge to the techniques based on location

fingerprinting is that the receivedsignal strength could be affected by diffraction, reflection,

and scattering in the propagation indoor environments.

There are at least five location fingerprinting-based positioning algorithms using

pattern recognition technique so far: probabilistic methods, k-nearest-neighbor (kNN), neural

networks, support vector machine (SVM), and smallest M-vertex polygon (SMP).


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4. PERFORMANCE MEASURENT OF GEOLOCATION SYSTEM:

It is not enough to measure the performance of a positioning technique only by

observing its accuracy. Considering the difference between the indoor and outdoor wirelessgeo location, we provide the following performance benchmarking for indoor wireless

location system: accuracy, precision, complexity, scalability, robustness, and cost.

4.1. Accuracy:

Accuracy (or location error) is the most important requirement of positioning systems.

Usually, mean distance error is adopted as the performance metric, which is the average

Euclidean distance between the estimated location and the true location. Accuracy can be

considered to be a potential bias, or systematic effect/offset of a positioning system. The

higher the accuracy, the better the system; however, there is often a tradeoff betweenaccuracy and other characteristics. Some compromise between suitable accuracy and other

characteristics is needed.

4.2. Precision:

Accuracy only considers the value of mean distance errors. However, location

precision considers how consistently the system works, i.e., it is a measure of the robustness

of the positioning technique as it reveals the variation in its performance over many trials. We

also notice that some literatures define the location precision as the standard deviation in the

location error or the geometric dilution of precision (GDOP), but we prefer it as thedistribution of distance error between the estimated location and the true location. Usually,

the cumulative probability functions (CDF) of the distance error is used for measuring the

precision of a system. When two positioning techniques are compared, if their accuracies are

the same, we prefer the system with the CDF graph, which reaches high probability values

faster, because its distance error is concentrated in small values. In practice, CDF is described

by the percentile format. For example, one system has a location precision of 90% within 2.3

m (the CDF of distance error of 2.3 m is 0.9), and 95% within 3.5 m; another one has a

precision of 50% within 2.3 m and 95% within 3.3 m. We could choose the former system

because of its higher precision.

4.3. Complexity:

Complexity of a positioning system can be attributed to hardware, software, and

operation factors. In this paper, we emphasize on software complexity, i.e., computing

complexity of the positioning algorithm. If the computation of the positioning algorithm is

performed on a centralized server side, the positioning could be calculated quickly due to the

powerful processing capability and the sufficient power supply. If it is carried out on the

mobile unit side, the effects of complexity could be evident. Most of the mobile units lack

strong processing power and long battery life; so, we would prefer positioning algorithmswith low complexity. Usually, it is difficult to derive the analytic complexity formula of


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different positioning techniques; thus, the computing time is considered. Location rate is an

important indicator for complexity. The dual of location rate is location lag, which is the

delay between a mobile target moving to a new location and reporting the new location of

that target by the system.

4.4. Robustness:

A positioning technique with high robustness could function normally even when

some signals are not available, or when some of the RSS value or angle character are never

seen before. Sometimes, the signal from a transmitter unit is totally blocked, so the signal

cannot be obtained from some measuring units. The only information to estimate the location

is the signal from other measuring units. Sometimes, some measuring units could be out of

function or damaged in a harsh environment. The positioning techniques have to use this

incomplete information to compute the location.

4.5. Scalability:

The scalability character of a system ensures the normal positioning function when

the positioning scope gets large. Usually, the positioning performance degrade when the

distance between the transmitter and receiver increases. A location system may need to scale

on two axes: geography and density. Geographic scale means that the area or volume is

covered. Density means the number of units located per unit geographic area/space per time

period. As more area/space is covered or units are crowded in an area/space, wireless signal

channels may become congested, more calculation may be needed to perform location

positioning, or more communication infrastructure may be required. Another measure ofscalability is the dimensional space of the system. The current system can locate the objects

in 2-D or 3-D space. Some systems can support both 2-D and 3-D spaces.

4.6. Cost:

The cost of a positioning system may depend on many factors. Important factors

include money, time, space, weight, and energy. The time factor is related to installation and

maintenance. Mobile units may have tight space and weight constraints. Measuring unit

density is considered to be a space cost. Sometimes, we have to consider some sunk costs.

For example, a positioning system layered over a wireless network may be considered tohave no hardware cost if all the necessary units of that network have already been purchased

for other purposes. Energy is an important cost factor of a system. Some mobile units (e.g.,

electronic article surveillance (EAS) tags and passive RFID tags, which are addressed later)

are completely energy passive. These units only respond to external fields and, thus, could

have an unlimited lifetime. Other mobile units (e.g., devices with rechargeable battery) have

a lifetime of several hours without recharging.


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5. COMPARING GEOLOCATION TECHNIQUES

The time of arrival technique estimate location by calculating the position of the

mobile by using circles centered on the mobile or the fixed transceivers. The time difference

of arrival does a similar technique using hyperbolas and does not require the knowledge of

transmit time from the transmitter. To create estimates of time, TOA and TDOA techniques

employ pulse transmission, phase information, or spread spectrum to arrive at locations of

mobile stations The time of arrival techniques are seen as superior to angle of arrival

techniques because the AOA method is not appropriate for indoor geo location systems. The

angle of arrival is suited for outdoor geo location but has poor accuracy. It has a low delay

but may be costly due to the need for antenna arrays that need to be installed in areas. The

signal strength method cannot be used in situations where the precision of the location needs

to be within accuracy of a few meters.


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6. EXAMPLE OF AN INDOOR GEOLOCATION SYSTEM

Commercial indoor geo location products have already appeared in the market. The abovefigure shows the example of an indoor geo location system. It is a pin point local positioning

system.

The system architecture is shown in the above figure. The Pin-Point system uses

simple structured tags that can be attached to valuable assets or personal badges. Indoor areas

are divided into cells while each cell being served by a cell controller. The cell controller is

connected to at most 16 antennas located at known positions. To locate tag position, cell

controller transmits 2.4GHz spread spectrum signal through different antennas in TDD mode.

Once receiving signals from the Cell Controller antenna network, tags simply change the

frequency of the received signal to 5.8GHz and transmit back to the Cell Controller with tag

ID information phase-modulated onto the signal. The distance between the tag and antenna is


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determined by round trip time of flight. With the measured distances from tag to antennas,

the tag position can be obtained in the same way as in TOA method. A host computer is

connected to cell controller through TCP/IP to manage the location information of the tags.

Since the cell controller generates the signal and measures the round trip time of flight, there

is no need to synchronize the clocks of tags and antennas.

The multipath effect is one of the limiting factor of the indoor geo location system.

Without multi path signal components, the time of arrival(TOA) could be easily determined

from the triangular auto-correlation function of the spread spectrum signal.


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7. APPLICATIONS

Positioning and tracking applications:

Commercial: finding in-demand equipments, in-need personnel, such as elderly, children

and patience

Military and public safety: positioning and guiding soldiers, firefighters in mission

Location-aided techniques:

Location-aware routing, location-aided handoff, location-aided network planning

Location-based services:

Location sensitive billing, location aware services, location specific advertisement


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8. FUTURE TREND

Future trends of wireless indoor positioning systems are as follows.

1) New or hybrid position algorithms are needed. A few of the works have already been

started supporting such algorithms. For example, a calibration-free location algorithm based

on triangulation, triangular interpolation and extrapolation (TIX). A hybrid algorithm is

presented in for indoor positioning using WLAN that aims to combine the benefits of the RF

propagation loss model and fingerprinting method. The selective fusion location estimation

(SELFLOC) algorithm infers the user location by selectively fusing location information

from multiple wireless technologies and/or multiple classical location algorithms in a

theoretically optimal manner.

2) Internetworking of different wireless positioning systems is a research and practical topicin order to extend the positioning range.

3) Wireless combined with other technologies such as optical (e.g., IR), inertial, dc

electromagnetic and ultrasonic for indoor location is another trend. How to combine these

technologies into a practical system is a topic of sensor fusion.

4) How to deploy sensors to improve the positioning accuracy, how to finish deploying

wireless positioning system in a short time, especially for emergency responder application.

5) Wireless indoor location using UWB (from 3.1 to 10.6 GHz) techniques and indoorpositioning using mobile cellular network are other promising research topics.

6) How to integrate indoor and outdoor positioning system is another area of research. This

integration may help in developing more efficient and robust detection systems for

positioning of mobile computing nodes. In this case, a mobile node will be tracked indoor or

outdoor using the same detection system.


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9. CONCLUSION

This paper surveys the current indoor positioning techniques and systems. Different

performance measurement criteria are discussed and several tradeoffs among them are

observed. For example, the one between complexity and accuracy/precision needs careful

consideration when we choose positioning systems and techniques for different applications

environments such as warehousing, robotics, or emergency. Usually, location fingerprinting

scheme is better for open areas while Active RFID is suitable for dense environments. In

terms of scalability and availability, these positioning techniques and systems have their own

important characteristics when applied in real environments. The choice of technique and

technology significantly affects the granularity and accuracy of the location information.


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