Wireless Sensor Network, 2009, 1, 434-445
doi:10.4236/wsn.2009.15052 Published Online December 2009 (http://www.scirp.org/journal/wsn).
Copyright © 2009 SciRes. WSN
Localization of a Target with Three Degrees of Freedom
Using a Low Cost Wireless Infrared Sensor Network
Analogies S.A., Patras Science Park, Platani-Rio, Patras, Greece
Email: {nikos.petrellis, fotios.gioulekas, michael.birbas, john.kikidis}@analogies.eu
Received August 10, 2009; revised Sept ember 14, 2009; accept ed September 15, 2009
The estimation of the position of a mobile target on a plane as well as its orientation is an important aspect
for many applications. The indoor or outdoor localization of such a target has been widely addressed in the
literature but if a third degree of freedom like rotation has to be also taken into consideration the difficulty in
estimating the target position and orientation is significantly increased. A network consisting of only a small
number of low cost infrared transmitters/receivers is used in this paper to estimate the position of a mobile
target on a plane as well as its draft orientation with an angular step of 45o or less. The distance and orienta-
tion estimation is based on the success rate that infrared patterns are retrieved at the target. This success rate
parameter is calculated by simple ultra low cost microcontrollers. The architectural complexity and cost of
the overall localization system is significantly lower than other approaches without sacrificing speed and ac-
curacy. An error correction scheme like Turbo decoding is applied in order to increase the reliability and sta-
bility of the results by correcting burst errors introduced by real time noise.
Keywords: Position Estimation, Localization, Infrared Sensor Networks, Turbo Codes
1. Introduction
The applications where indoor localization is important
concern robotics, automation, virtual reality and perva-
sive computing environments. Although knowing the
position of a moving target on a plane is very important
it would be valuable for a large number of applications if
the orientation of the target could also be estimated. For
example, a system guiding a robot, a handicapped person
or a person wandering in a virtual museum, should also
be aware of the draft at least orientation of this robot or
person. Although several approaches have been proposed
for the position estimation of targets with 2 degrees of
freedom, there are not many general solutions for targets
with 3 degrees of freedom.
A rather complicated method used by the robots in
order to familiarize with an unknown environment is
based on processing the images captured by cameras that
are mounted on the robots in order to recognize land-
marks and their distance [1]. Conclusions extracted from
image processing are combined with other localization
approaches [2] in autonomous robotics applications. Sto-
chastic processing can also help in this case for the vali-
dation of an estimated position. It is obvious that image
processing requires powerful and complicated processing
units leading to rather expensive localization solutions.
Measuring the time of the flight of a reflected impulse
wave or the strength of a signal are ordinary methods
used for the indoor or even outdoor localization. Optical
or Laser beams can be used to scan the surrounding area
detecting the distance of walls or other obstacles. The
cost of this solution is higher since very short time inter-
vals have to be measured with high precision [1,3]. Ul-
trasonic signals can offer a lower cost alternative to this
approach since the sonar waves travel with much lower
speed than light [4,5]. A drawback concerning the use of
ultrasounds is that this type of signal is not directional
enough and it is difficult to isolate the sonar transmitter
from the receiver in order to ensure that only the re-
flected signal will be taken into consideration during the
distance measurement. Localization systems based on
ultrasonic waves often estimate distances by measuring
phase shifts of the original and the reflected signal,
which also requires processing of high precision and
The signal strength of multiple transmitters surround-
ing the target can also provide an indication about the
target position using a triangulation method [6,7]. This
technique has already been adopted in cellular telephony,
Wireless Local Access Network (WLAN) or Bluetooth
Magnetic fields have been adopted for the accurate
non-contact control of the position and orientation of
tools and medical instruments in short distances (up to a
few centimeters) [8,9]; Distance estimation of up to 10m
using magnetic fields has been reported in [10].
Passive infrared sensors are used by mobile targets in
order to avoid obstacles while active infrared tranceivers
are employed similarly with laser or ultrasonic beams to
detect the distance of the target from walls, obstacles etc
[11,12]. Another interesting use of infrared light is the
profiling of the surface of an object by recognizing its
texture (used for distinguishing metal from plastic sur-
faces etc) [13,14].
Low cost infrared tranceivers have been used in order
to estimate the position of a moving target with 2 degrees
of freedom with an absolute error of less than 20cm in
[15–18]. The reception quality of the digital patterns that
are sent by at least two transmitters that are placed
around the covered area is exploited for the estimation of
the position of a receiver that can move on a plane with-
out rotating. A calibration procedure before real time
operation is required in order to familiarize the target
with the area. During this calibration stage, the target
visits predetermined positions and enumerates the recog-
nized patterns in a period of time in order to estimate the
success rate of the reception. A position identity can be
formed by the success rates of the various pattern types
that are employed. During real time operation, a position
identity is constructed in the same way for the current
target position and is compared to the identities that were
estimated during the calibration stage. The closer posi-
tion that was visited during the calibration is selected. A
regression technique can help reaching a more accurate
estimation of the real target position.
Instant noise that has not been taken into consideration
during the calibration stage can reduce the speed and
accuracy of the position estimation. Several rules can be
applied to validate the results of the position estimation
procedure [16]. If a positive estimation is characterized
by these rules as unacceptable, it is discarded and a new
estimation is initiated. Error Correction techniques have
also been employed by the authors [17] in order to re-
duce the effect of instant noise and speed up the estima-
tion procedure. The interleaving process employed in
Turbo decoding can minimize the effect of the burst er-
rors caused by the instant noise [19,20]. When the at-
tenuated signal is received at the target, it is corrected by
a decoder that can be implemented either in software or
by dedicated hardware [21,22]. Since our localization
approach is based on the quality of the received signal,
the intension is to minimize the effect of the burst errors
caused by instant noise through the selected Error Cor-
recting method rather than fully recover the initial pat-
terns. A description of how an additional sensor at the
side of the target can be used to provide an orientation
indication has been given in [18].
In the present work we modify some architectural fea-
tures of the infrared transmitters and receivers in order to
obtain a draft indication of the target orientation in 45o
angular steps i.e, 8 potential directions. It will also be
explained how narrower angular steps and consequently
more accurate orientation estimation can been supported.
The position and orientation estimation in the architec-
ture that will be presented here, takes place in two phases:
the draft orientation is initially estimated by classifying
the retrieved success rates as strong or weak and then the
target position coordinates related to an infrared trans-
mitter are estimated using the exact success rate values.
The designed localization system covers a large area
with a small number of low cost transmitters/receivers
since it is based on a simple digital processing method
that merely counts patterns. A very good trade off be-
tween cost, area covered, accuracy and speed is achieved.
The architecture of the infrared transmitting and re-
ceiving devices along with the estimation methods used
in our previous approaches are described in Section 2.
The topology used in the present work and the posi-
tion/orientation estimation method is described in Sec-
tion 3. Finally, the experimental results will be discussed
in Section 4.
2. System Architecture
2.1. Infrared Transmitter (Irtx)
The architecture of the infrared transmitters (IRTX) that
are used in the proposed localization system is presented
in Figure 1. A processing unit generates both the patterns
and the carrier that are mixed and amplified before they
are transmitted by one or more infrared emitting diodes.
In [15,16,18] the infrared patterns transmitted by an
IRTX device had the form that appears in Figure 2a.
Each pattern type consists of a number (i) of pulses that
have a duration inversely proportional to the number i.
IRTX processing
Figure 1. Architecture of an IRTX device.
Carrier gen-
Copyright © 2009 SciRes. WSN
Copyright © 2009 SciRes. WSN
Preamble MOD2 MOD2MOD4
Pause Pause
Preamble 00000001 00000001 01101000
1 bit
Figure 2. Infrared pattern formats consisting of a preamble followed by several MODi patterns (a) or signatures (b).
Parity Bits
Stored in the memory
of the IRTX processing
One clock DelayExclusive OR
Figure 3. Recursive systematic convolutional (RSC) encoding used by the IRTX devices.
Such a pattern is designated as MODi. This type of for-
mat was chosen because patterns with higher number of
short pulses are recognized with lower success rate than
patterns consisting of lower number of long pulses.
Moreover, the receiver may simply count rising or falling
edges between long pause intervals in order to recognize
a pattern type. Each IRTX device initially transmits a
preamble and then, a specific number of codes from each
pattern type. After the transmission of all the supported
pattern types, this procedure is repeated by transmitting a
new preamble.
ceiver can lead to faster implementations with higher
precision results as discussed in [17]. Using this type of
patterns, the receiver sensors have to take samples at
regular time intervals instead of merely counting rising
or falling edges. A preamble is initially transmitted fol-
lowed by a synchronization sequence (SYNC). The
preamble is a long pause period while the SYNC is a
small sequence of identical pulses used by the receiver
for synchronization. Then, all the supported pattern
codes are transmitted. Patterns of this type will be called
henceforth “signatures” since they identify their trans-
mitter. Although multiple signatures with smaller length
can be used, a single signature with 160-bit length can
lead to a better trade off between speed and accuracy as
discussed in [17].
The digital pattern signal is mixed with a carrier be-
fore driving the infrared emitting diodes. The carrier is
used in order to avoid interference from other infrared
sources like sunlight. More than one infrared emitting
diodes can be connected in parallel and placed in a cir-
cular arrangement in order to cover a wider area.
The signatures are transmitted along with their parity
bits that have been generated through a proper encoding.
The receiver is aware of the supported patterns or signa-
tures and simply enumerates the recognized ones i.e.,
those that have not been distorted. For this reason, pre-
Using a pattern format that consists of a constant num-
ber of bits that have equal duration as shown in Figure 2b
in order to employ an error correcting method at the re-
encoded signatures and parity bits are stored in the
memory of the IRTX processing unit. The Recursive
Systematic Convolutional (RSC) encoding described by
Figure 3 is used off line to generate the parity bits that
correspond to the supported signatures. The polynomial
describing this encoding is:
(1+ D2)/(1+D+D2) (1)
2.2. Infrared Receiver (IRRX)
The architecture of the infrared receiver devices (IRRX)
used, is shown in Figure 4. The infrared sensors are con-
nected to a bandpass filter to select only the infrared sig-
nals modulated at the specific carrier frequency used.
Then, an integrator rejects the carrier and the pure pat-
tern signal is recognized by the processing unit of the
receiver. If the target moves with 2 degrees of freedom,
two infrared sensors (IRRXA and IRRXB) are adequate
[15–17]. If the target is also allowed to rotate a third
IRRX sensor may be helpful as discussed in [18]. Nev-
ertheless, in the next section we will describe how only 2
IRRX sensors can also be used in order to get an indica-
tion of the orientation of the target if they are placed at a
proper angle.
The IRRX processing unit marks the start and the end
of the signature and parity bit parts. The next stage is the
application of a Turbo Decoding algorithm in order to
minimize the effect of the burst errors caused by instant
noise that has not been taken into consideration during
the calibration stage [17]. It is noticed that the aim of the
applied Turbo decoding method is to smooth the error
occurrence instead of correcting all the errors. More spe-
cifically, the interleaving used by the Turbo decoders,
scatters the burst errors throughout the length of a signa-
ture making easier their correction. The turbo decoder
can be implemented at the host computer where the ap-
plication that exploits the position estimation results is
running. If the IRRX processing unit is a more compli-
cated one supporting all the applications required by the
target, the Turbo decoding can be implemented in a low
level language like assembly. Another option is to embed
a special Turbo decoding peripheral like the one de-
scribed in [21]. This option has a higher cost but would
significantly speed up the decoding procedure.
2.3. Turbo D ecoding Implementation A lternatives
The state machine and the Trellis diagram that corre-
spond to the encoding scheme of Figure 3 are presented
in Figures 5a and 5b respectively. The interleaver used is
of the random type and it is shown in Figure 6 since a
lower Bit Error Rate (BER) can be achieved in this way [17].
The Turbo Decoder used consists of two Soft In Soft
Out (SISO) decoders each one of them operating alterna-
tively on the un-interleaved and interleaved input signa-
tures respectively (see Figure 7). Each SISO decoder
consists of identical blocks that implement a stage of the
Trellis diagram (Figure 8). A SISO decoder generates
extrinsic information that is used as intrinsic input by the
other SISO decoder at the following iteration. The de-
coding stops and the outputs are settled after a specific
number of iterations.
The parameters a_S(t) and b_S(t) of Fig. 8 are branch
metrics of the Trellis diagram qualifying how possible is
a path leading to state S of the stage t. Int(t), Ext(t) and
U(t) are the intrinsic, the extrinsic info and the output of
this stage. The inputs P(Yu|Xu) and P(Yp|Xp) are the
Host Computer
Figure 4. Architecture of the IRRX device consisting of two sensors and a processing unit that communicates with a Host
IRRX processing unit
IRRXA Integrator
Filter (Carrier
SISO Decoder
Turbo Decoder
Copyright © 2009 SciRes. WSN
(a) (b)
Figure 5. State machine (a) and Trellis diagram (b) for the RSC encoder of Figure 3.
160-bit Signature
b159-b158-b157-… b2-b1-b0
b159-b158-b157-… b2-b1-b0
Figure 6. The bit positions of the original signature are mapped to random positions in the interleaved one (random inter-
Signature+Parity SISO DECODER 1
Figure 7. Turbo decoding scheme.
probabilities to receive symbols Yu and Yp given that
the input Xu and the parity Xp has been transmitted. The
lower BER is achieved if the decoding is performed by
the Sum-Product Algorithm (SPA) that is described by
the relations of Table 1.
The analog implementation of a Turbo Decoder pre-
sented in [21] is an example of how the decoding needed
by the localization system can be implemented as a
processing unit peripheral. If simpler operations than
multiplication are needed in order to have a less compli-
cated hardware implementation of the Turbo decoding,
the Max-Log MAP or the Min-Sum algorithm can be
employed [22]. Their relations are derived by the equa-
tions of Table 1 if a logarithmic operation is applied to
Interleaver Interleaver
P(Yp/Xp) Extrinsic Info
Intrinsic Info
Intrinsic Info
Extrinsic Info
Copyright © 2009 SciRes. WSN
Figure 8. Implementation of a Trellis stage.
Table 1. The relations of the SPA algorithm.
Block Calculation
'Pr( ()|())Pr(()|())
iju upp
yt xtiyt xtj
B '()
ijij i
Int t
, , {0,1}ij
C '
_()_'( 1)
D '
_()_( 1)
bSt bSt
E '
'(1)_( )
ij ijS S
F ()Pr( ()|())
Ext tIytxtj
{0 ,1}i
G ()
Ut I
, {0,1}i
both sides and the following identity is taken into con-
)1ln(),min()ln( || xyyx eyxee  (2)
The second term of the right side of Equation (2) is
often neglected with a penalty of about 0.5dB in the
achieved BER. In the present work the Turbo decoding
scheme is implemented by software at the Host Com-
puter. For this reason, SPA algorithm was chosen in or-
der to achieve the best possible error correction.
2.4. Topologies for Position Estimation
In [15] the success rates of various pattern types at regu-
lar distance and angular steps was measured as shown in
Figure 9. The position of the target was represented with
polar coordinates related to an IRTX device. For exam-
ple, Figure 10 shows the success rate curves of pattern
types MOD2, MOD5, MOD6 and MOD9 at 2m distance
and angular displacement ranging from –90o to +90o. If
the target resides at a distance different than 2m, the
success rates that are measured for MOD2, MOD5,
MOD6 and MOD9 may be the r2, r5, r6 and r9 of Fig. 10,
respectively that do not converge to the same angle. On
the contrary, if it resides at a 2m distance and the meas-
ured success rates are r’2, r’5, r’6 and r’9, then all these
success rates converge to the same angle (35o or +35o).
A second IRTX device is used to break the symmetry
at the right and the left side of the reference infrared
transmitter. Such curves can also be used to give an in-
dication about the orientation of the target since the posi-
tion estimation is expressed in polar coordinates (dis-
tance, angle) as described in [18].
The smooth success rate behavior designated by the
curves of Figure 10 is valid only if the multiple IRTX
devices do not transmit concurrently in order to avoid
scrambling. This restriction slows down the localization
procedure since the estimation time needed is longer in
order to allow the target to receive the necessary pattern
codes from all the neighboring IRTX devices.
Figure 9. Topology for polar coordinate estimation
Figure 10. Success rates at 2m distance from IRTX.
Copyright © 2009 SciRes. WSN
The topology proposed at [16,17] overcomes this re-
striction since the target moves with 2 degrees of free-
dom on a virtual grid plane as shown in Figure 11. The
transmitters are positioned at the borders of the covered
area. During the calibration stage, the target visits the
grid nodes and stores the retrieved success rates. At real
time operation, the closer grid node is selected after
comparing the current success rates with the stored ones.
An interpolation method can give a more accurate ap-
proximation of the real target position expressed in Car-
tesian coordinates. A larger area can be covered if more
than two IRTX devices are properly positioned. An ex-
ample of how the success rate of a specific pattern type
changes within a 1m2 area covered by 2 IRTX devices is
shown in Figure 12. Comparing Figure 10 with Figure 12
we conclude that the success rates change in a more ran-
dom way when the IRTX devices transmit concurrently.
Figure 11. Grid topology.
Figure 13. Example positioning of the IRTX devices in or-
der to estimate both distance and orientation.
Figure 14. Distinct orientations
3. Proposen Method
of the Targe t Posi tion and Orientation
roposed in this paper for obtaining an orientation indi-
d Topology and Estimatio
e detailed topology and position estimation m
cation except from the Cartesian coordinates is presented
in this section. The IRTX devices have the architecture
shown in Figure 1 and transmit the stored, pre-encoded
160-bit signatures concurrently. They can be positioned
as shown in Figure 13, in order to cover a corridor or a
hall. Each IRTX device transmits a different signature.
An IRTX device should transmit different signatures
from its neighboring IRTX devices. Multiple IRTX de-
vices can be used to cover the desired area transmitting
alternatively only 3 types of signatures. For example, the
IRTX3 device at the bottom of Figure 13 should be fol-
lowed by IRTX1, IRTX2 etc in order to extend the area
covered. A virtual grid is assumed to cover the area of
this corridor or hall. The nodes of this grid are visited
during the calibration phase by the target in order to
measure the success rates at various orientations and
familiarize with the area.
igure 12. Example success rate in a 1m2 area when the
IRTX devices tr ansmit c o nc urrently.
Copyright © 2009 SciRes. WSN
Copyright © 2009 SciRes. WSN
ept from a different angle the
ce covers
e success rate in
nsisting of different patterns of 1’s and 0’s in
The two IRRX sensors that reside on the target, form a
135o angle in order to acc
signatures sent by neighboring transmitters. This is use-
ful in order to define unique orientation identities. The
received signatures along with their parity bits are cor-
rected by a Turbo decoder that is implemented in soft-
ware at the Host computer at this prototype level. The
algorithm employed by the Turbo decoder is the Sum-
Product. The system can distinguish the 8 draft orienta-
tions of the target that are shown in Figure 14.
An indication about the orientation of the target can be
extracted if we assume that each IRTX devi
ree adjunct regions in front of it and at its left and right
side. A target residing at the region in front of this spe-
cific IRTX device may receive strong or weak signal
from it and a weak signal from either of its neighboring
IRTX devices. If the target resides at the left of this spe-
cific IRTX device it can receive strong or weak signals
from it and its neighboring IRTX device at the left. If the
target resides at the right of this specific IRTX device it
can receive strong or weak signals from him and his
neighboring IRTX device at the right.
In order to define when a signal will be characterized
as strong or weak we should define th
e present architecture. As already mentioned a 160-bit
signature is transmitted by each IRTX device. This sig-
nature is accompanied by a 160-bit parity that has been
generated after encoding the initial signature using the
RSC encoder of Figure 3. The signature is interleaved by
a random interleaver and encoded by the same RSC en-
coder. An IRTX device transmits the signature in its
original and interleaved form as well as all the parity bits
i.e., a total of 640-bits are sent (Rate=1/4). The 480 re-
dundant bits are used by the receiver in order to correct
the burst errors that occurred during the transmission due
to instant noise and recover the original 160-bit signa-
The original 160-bit signature is split in ten 16-bit
parts co
der to differentiate the difficulty in correcting each one
of these parts. For example, the correction of the signa-
ture parts 0x5555 and 0xEEEE may not have the same
success. The number of signature part bits that match the
expected ones after the Turbo Decoding, define the suc-
cess rate (SRi) for this signature part i. Its value may
range from 0 to 16. The success rate of the whole signa-
ture is a complex identity consisting of the 10 individual
success rates SRi. The Averaged Success Rate (ASR) of
a signature is defined as:
(SRASR 10/)... 910 SRSR
The success rate of more than one sample can be a
Figure 13
tions used in Table 2,
Table 2. Orietation rules.
R1 R2 R3
aged to get a more reliable estimation. An IRTX signal
is characterized weak if the ASR of its signature is less
or equal to 5 and strong if it is higher than 5.
Focusing on the regions R1, R2 and R3 of
d assuming that the target is at the specific position
and has a North orientation we can state and experimen-
tally prove that the IRRXA receives a weak signal from
IRTX3 and IRRXB receives a strong signal from IRTX1.
In the same way the rules listed in Table 2 can be ex-
tracted for the topology of Figure 13. The columns A and
B of this table designate the strength of the signals re-
ceived from each IRTX device on the sensors IRRXA
and IRRXB respectively. The letters W and S mean
Weak and Strong respectively, while the number follow-
ing this letter corresponds to the IRTX device transmit-
ting this signal. For example, W3 means that a weak
signal is received from IRTX3.
The draft signal characteriza
rm an orientation identity of the target. Hence, the tar-
get determines its orientation in a specific region as a
first stage of the localization procedure. Unfortunately,
the East orientation of R1 can be confused with the
South East one of R2, as we can see from this table.
Moreover, two target orientations of R2 and R3 (the ones
with the shaded background) can also be confused. The
higher number of orientations that can be potentially
confused in this case is owed to the corner of the corridor
and the IRTX2 and IRTX3 positioning. Such a problem
can be resolved at a certain extent if we know some a
priori restrictions (maximum/minimum speed, steering
rules etc) in the move of the target as discussed in [16].
After determining the orientation of the target, a spe-
fic set of signature success rate maps like the one pre-
sented in Figure 12 are used to select the closer grid node
to the target. More specifically, the success rates SRi
Orientation B OrientationB OrientationB A A A
N W3 S1 N W23 +WW1 N S2 W2
NE W3 1+WW1 NE W2 S1 NE W2 W3
E W1 W3 E 1+WW2 W1 E W2 W3 1+W
SE S1 W3 SE W1 W3 SE 1+WW2 W1
S W1 S3 S S1 W3 S W3 W2
SW W3 SW W3 SW W3 1+WW3 W1 2+W1+WS2
W W3 W3 1+WW W1 W2 W W2+W3 W1
NW S3 W1 NW W3 W1 NW W1+W2 W2
retrieved at the currensition are comparede cor-
The parameter SR is the success rate of the signature
emphasized that during the calibration
low, in
4. Case Study-Discussion
onsidering the first stage of the localization procedure
If theocalization procedure is suc-
ed to be faced if more than 8 dis-
uency used at the prototype level is a
ntal target track in three re-
t po to th
responding stored ones from the calibration phase in or-
der to select the closest grid node. The comparison is
based on the following equation:
iijij SRSRabsD
rt i at the current target position. SRij' is the success
rate of the corresponding part i, retrieved during calibra-
tion at the node j when the target had the orientation
recognized at the first stage of the localization procedure.
A different Dj parameter is estimated for each grid node
and the node that has the smallest Dj value is selected as
the closest one. This is the second stage of the localiza-
tion procedure.
It should be
ase, a different set of success rate maps should be
stored for each orientation. For example, if a 1.8x1.8
meter region is assumed to be covered by a 30x30cm
grid, then 25 grid nodes should be visited by the target
without changing his orientation. A total of 8x25=200
success rate measurements should be carried out during
the calibration phase to familiarize with a specific region,
since 8 distinct orientations are considered.
A third localization procedure stage may fol
hich the actual target position can be further approxi-
mated by an interpolation method as described in [16].
This interpolation method assumes that the success rate
parameters change linearly between neighboring grid
nodes. This assumption is valid if the grid node distance
is short enough (less than 30cm).
where the draft target orientation is decided, the suc-
cessful estimations approach a level of 100%. Some fail-
ures may occur at positions or orientations where the
measured success rates are close to the limit between the
Weak and Strong signal characterization. Of course, if
the orientation estimation fails, the second and third
stage of the localization procedure will possibly fail too,
since a wrong success rate set of maps derived from the
calibration stage will be used.
1st stage of the l
ssful, then the expected absolute error at the position
estimation that is carried out at the 2nd and 3rd localiza-
tion stage should be comparable to the one achieved at
[16]. The worst error in this case was equal to the grid
node distance (20cm). Having in mind that a longer grid
node distance (30cm) has been selected in the present
work, the worst position estimation error is 30cm.
The worst error may be reduced if shorter grid
stance is selected. The 3rd localization procedure stage
(interpolation) would also be more efficient in this case
since the success rate changes more linearly between
neighboring nodes with 20cm distance than those that
have a 30cm distance. Nevertheless, a time consuming
calibration stage would be necessary since the number of
nodes that have to be visited is extremely higher in this
case. Moreover, if a large number of grid nodes are con-
sidered, there is a higher possibility that several nodes
with similar success rate identities that can be confused
at real time operation leading to false position estima-
tions will be existed.
Similar problems ne
ct target directions are considered. For example, if we
try to distinguish between 16 directions the number of
grid nodes that would have to be visited during calibra-
tion would be doubled. More than two signal characteri-
zations would be required in this case to form different
orientation identities (e.g., weak, medium, strong) but if
a higher number of signal characterizations are used,
then it is more difficult to distinguish the limits between
them and more errors will appear when determining the
target orientation.
The carrier freq
w standard frequency of 38KHz that is adopted by
commercial IR remote control devices. The estimation of
the target orientation and position requires more than 1
sec in this case. This time can be reduced to less than
50ms if a higher carrier frequency is used (e.g., 1MHz)
[16,17]. In this case, a custom bandpass filter and carrier
rejecting circuit is required at the side of the receiver
while the receiver processing unit should operate at
higher clock frequencies (at least 20MHz) to sample ef-
ficiently the IRRX sensors.
In Figure 15 an experime
ons (R1, R2, R3) neighboring to the IRTX1 transmitter
Figure 15. Experimental target track.
Copyright © 2009 SciRes. WSN
Real Position
e 3. Experimental target localization res
Experimentally Estimated Position
Coordinates Orientation
Posit. Coordinates Orient CoOr ab
Ident. ord. Stab ient. St ASR ASR Used/
p1 R1(80cm, N R 1/3 N 3/3 2(3) 6(1)
p2 R, N R1(m, 2/3 N 3/3 3(3) 10(1)
p3 R E R, 2/3 E 3/3 1(1)
p4 R NE 3/3 NE 3/3 1(2)
150cm) 7(1) N
p5 R SE R 1/3 E 2/3 2(1)
80cm) 2(120cm,
90cm) 2(1)
1(2) Y
p6 R, SE R, 2/3 SE 2/3 3(1)
p7 R E R, 1/3 E 3/3 5(1)
150cm) 4(2) Y
Table 4. Localization method comparison.
Method Complexity/Cost Range Accuracy/
Orienation tation Indic
This work Very Low 16m2/transmitter Yes
(<45 degrees)
Acoustic of flight/ Medium Tens of meters
sound (time
reception angle)
(5 degrees
(t) Medium Tens of meters
ime of flight
(phe) High 10-20m Few ceters/
ase differenc
Yes (<20degrees)
(timht) High One hundred meters or longer I e of flig
IVery High Tens of meters Few
mage Processing centimeters /
RF(Received Signal Strength Medium 2-3m
of centimete
presented. Each one of these regions has an area of
positions where the Forward Error Correcting method
the right decision about the orienta-
ientation was
1.8mX1.8m and is covered by a virtual grid with 30cm
node distance. It should be noted that a single IRTX de-
vice can cover an area of up to 16m2 but the areas cov-
ered by two neighboring IRTX devices should overlap in
order to define rules like the ones presented in Table 2. It
will also be assumed that the lower left corner of a region
Ri has coordinates Ri(0,0) as shown in Figure 15.
Table 3 lists the real target coordinates and ori
ns and the experimentally measured ones. Three sam-
ples have been examined for every position and the ex-
perimental coordinates and orientation listed in Table 3
are the best ones achieved from these 3 samples. The 5th
and 7th columns are an indication of the results’ stability
denoted as x/3 where x shows how many samples among
the 3 considered leaded to the selected best results. In the
ASR columns the Average Success Rates measured by
IRTXA and IRTXB are listed and these values were used
for the estimation of the best position/ orientation men-
tioned in this table. The number in the parentheses in
these two columns refers to the IRTX device that corre-
sponds to the listed ASR. The last column shows the
(FEC) and the 3rd stage of the localization procedure im-
proved the results.
It is worth mentioning the case of p5 where the system
was unable to reach
n of the target (decided E instead of SE). Conse-
quently, the estimated coordinates could not be close
enough to the real target position. The signal characteri-
zation extracted at p5 did not match any orientation and
the conclusion for the E orientation was reached by ig-
noring the Weak indication from IRTX1.
The stability of the orientation results is also very
good since in most positions the right or
nstantly selected. The stability of the position coordi-
nates is slightly worse, but rules like the ones described
in [16] that were not used in the present case study can
assist the system to improve its results. In many cases,
the Turbo Decoding method led to better results by dis-
carding instant noise effects as indicated by the last
column of Table 3. Finally, the 3rd localization stage was
helpful only once. This is due to the fact that the grid
node distance used was relatively long.
Copyright © 2009 SciRes. WSN
entioned the cost
m capable of determining the orien-
tatn of a target in one of 8 possible directions with high
s that can be distinguished at the various target
. Delahoche, E. Brassart and C. Drocourt,
“Self localisation: A new uncertainty propagation archi-
niques,” A.K. Peters Ltd Welles-
rol by considering map and motion uncer-
botics and Autonomous Systems,
and T. Aoyama, “Dolphin: A
Robotics based location sensing using wireless
ed services for portable wireless
with 5deg of freedom using a 2d array
ors Jour-
tion,” Wiley Jour-
an autonomous vehicle with low level sensors,”
rties of targets using simple
obile robots,”
Table 4 provides a general comparison between vari-
ous localization methods. As already m
the proposed solution is extremely low since it is suf-
ficient to count the received patterns by incorporating
slow-speed microcontrollers without the necessity to
employ high-precision sensors. Moreover, a large area
can be covered by a small number of infrared transmit-
ters since a single transmitter can cover a 16m2 area. The
accuracy in the measured distance or the orientation is
quite good for several applications. Acoustic sound and
ultrasonic waves can achieve a better accuracy and they
can also provide some orientation indication but they
require significantly higher cost sensors and processing
units in order to discriminate small time intervals or
phase shifts. Laser beams can achieve a very good accu-
racy and can cover a wide range but they have signifi-
cantly higher cost since they require much faster proc-
essing units. They also provide orientation indication in
an indirect manner since they are used to measure a dis-
tance at a specific direction. Image processing also re-
quires very high cost complicated sensors and processing
units in order to analyze a captured image. Orientation
can be concluded indirectly if the target is aware of his
environment by detecting specific landmarks in his di-
rection. Finally, Received Signal Strength Indicator
(RSSI) can also provide a draft distance estimation of the
RF receiver.
5. Conclus
A localization syste
estimation stability was described in this paper. Beside
this draft orientation, the system is also capable to de-
termine the position of the target with a maximum abso-
lute error less than 30cm. The estimation method is
based on a simple enumeration of infrared patterns in
order to estimate a success rate parameter that qualifies
the signal of a specific transmitter. Each infrared trans-
mitter covers an area of up to 16m2 and multiple trans-
mitters with partially overlapping range can be used to
cover the desired area. The infrared patterns are received
by two sensors that are positioned at an angle of 135o on
the target in order to differentiate the quality of reception
from different transmitters. Simple commercial compo-
nents have been used at the side of the transmitter and
the receiver, since no high precision analog measure-
ments are required, leading to ultra low cost implementa-
Future work will focus on increasing the distinct di-
sitions without sacrificing the accuracy, stability and
speed of the estimation method. The localization of a
target in 3 dimensions with 4 degrees of freedom (in-
cluding rotation) using a similar method than the one
presented here will also be investigated.
6. References
[1] A. Clerentin, A
tectur,” Elsevier Robotics and Autonomous Systems, Vol.
51, pp. 151–166, 2005.
[2] J. Borenstein, B. Everett and L. Feng, “Navigating mobile
robots: Systems and tech
ley, 1996.
[3] J. Miura, Y. Negishi, and Y. Shirai, “Adaptive robot
speed cont
tainty,” Elsevier Robotics and Autonomous Systems, Vol.
54, pp. 110–117, 2006.
[4] P. Smith and K. Zografos, “Sonar for recognizing the
texture of pathways,” Ro
Vol. 51, pp. 17–28, 2005.
[5] M. Minami, Y. Fukuju, K. Hirasawa, S. Yokoyama, M.
Mizumachi, H. Morikawa,
practical approach for implementing a fully distributed
indoor ultrasonic positioning system,” Lecture Notes for
Computer Science (LNCS), Vol. 3205, pp. 347–365,
[6] A. Ladd, K. Bekiris, A. Rudys, L. Kavraki, and D. Wal-
lach, “
ethernet,” Wireless Networks, Vol. 11, No. 1–2, pp.
189–204, January 2005.
[7] C. Flora, M. Ficco, S. Russo, and V. Vecchio, “Indoor
and outdoor location bas
ices,” Proceedings of 1st IEEE International Work-
shop on Services and Infrastructure for Ubiquitous and
Mobile Internet, Columbus OH, pp. 244–250, June 2005.
[8] J. Kosel, H. Pfutzner, L. Mehnen, E. Kaniusas, T. Mey-
dan, N. Vazquez, M. Rohn, A. Merlo, and B. Marquardt,
“Non contact detection of magnetoelastic position sen-
sors,” Elsevier Sensors and Actuators A, No. 123–124, pp.
349–353, 2005.
[9] V. Schlageter, P. Besse, R. Popovic, and P. Kucera,
“Tracking system
of hall sensors and a permanent magnet,” Elsevier Sen-
sors and Actuators A, No. 92, pp. 37–42, 2001.
[10] E. Prigge and J. How, “Signal architecture for distributed
magnetic local positioning system,” IEEE Sens
nal, Vol. 4, No. 6, pp. 864–873, 2004.
[11] T. Jin, J. Lee, and S. Tso, “A new space and time sensor
fusion method for mobile robot naviga
nal of Robotics Systems, Vol. 21, No. 7, pp. 389–400,
[12] E. Bicho, P. Mallet, and G. Schoner, “Target representa-
tion on
International Journal of Robotics Research, Vol. 19, No.
5, pp. 424–447, May 2000.
[13] T. Aytac and B. Barshan, “Simultaneous extraction of
geometry and surface prope
infrared sensors,” SPIE Optical Engineering Journal, Vol.
43, No. 10, pp. 2437–2447, October 2004.
[14] G. Benet, F. Blanes, J. Simo, and P. Perez, “Using infra-
red sensors for distance measurement in m
Copyright © 2009 SciRes. WSN
Copyright © 2009 SciRes. WSN
n restriction rules for stabilizing the results
ection to infra-
the estimation of the po-
F. Seguin, D. Gnaedig, and M.
erez, “Trellis and turbo coding,”
irbas, A. Birbas, and G. Bilionis,
T. Prokop, C. Thomas, and
Robot Autonomy Systems, Vol. 30, pp. 255–266, 2002.
[15] Ν. Petrellis, N. Konofaos, and G. Ph. Alexiou, “Target
localisation utilising the success rate in infrared patter
recognition,” IEEE Sensors Journal, Vol. 6, pp. 1355–
1364, 2006.
[16] Ν. Petrellis, N. Konofaos, and G. Ph. Alexiou, “Using
future positio
of a noise sensitive indoor localization system,” SPIE
Optical Engineering Journal, Vol. 6, No. 46, Article No
067202, pp. 067202-1–067202–11, 2007.
[17] Ν. Petrellis, F. Gioulekas, M. Birbas, J. Kikidis, and A.
Birbas, “Use of interleaving and error corr
red patterns for the improvement of position estimation
systems,” in Proceedings of IEEE Conference Emerging
Technologies-Factory Automation, Hamburg, Germany,
pp. 888–891, September 2008.
[18] Ν. Petrellis, N. Konofaos, and G. Ph. Alexiou, “A wire-
less infrared sensor network for
on and orientation of a moving target,” in Proceedings
ICST-ACM Conference MobiMedia’07, Nafpaktos,
Greece, August 2007.
[19] M. Arzel, C. Lahuec,
quel, “Semi-iterative analog turbo decoding,” IEEE
Transactions on Circuits and Systems, Vol. 54, No. 6,
pp. 1305–1315, 2007.
[20] C. Schlegel and L. P
IEEE Series on Digital & Mobile Communication, Wiley
Interscience, 2004.
[21] F. Gioulekas, M. B
“Analog error-correcting decoders using SiGe BiCMOS
technology,” International Journal of Analog Integrated
Circuits and Signal Processing, Springer, Vol. 52, No. 3
pp. 117–132, October 2007.
[22] M. A. Bickerstaff, D. Garret,
C. Nicol, “A 24 Mb/s radix-4 logMAP turbo decoder for
3GPP-HSDPA mobile wireless,” in Proceedings IEEE
Conference International Solid-State Circuits, San Fran-
cisco, CA, pp. 150–151, February 2003.