Compensation of the Antenna Polarization Misalignment in the RSSI

Estimation

D. Polese, L. Pazzini, A. Minotti, L. Maiolo and A. Pecora

Consiglio Nazionale delle Ricerche - Istituto per la Microelettronica e Microsistemi (CNR-IMM),

Via del Fosso del Cavaliere 100, 00133, Roma, Italy

Keywords: Wireless Sensor Network, ZigBee, RSSI, Polarization Angle, Accelerometer, Inclinometer Sensor, Indoor

Localization.

Abstract: The diffusion of wireless sensor networks has allowed the development of a plethora of indoor localization

algorithms based on these technologies. Also if several radio signal features have been exploited in order to

estimate the position, the Received Signal Strength (RSS) is probably the most used. RSS depends, in

addition to the distance, also on multipath transmission, barriers and non-idealities of antenna. Differences

between ideal and real omnidirectional transmission patterns or polarization angle misalignment can

strongly affect the RSS value impairing the following localization algorithm.

In this paper, an algorithm to compensate the dependence of the RSS on the angle among the antennas is

proposed and tested. The experimental results prove the goodness of the approach and the possibility of

using this algorithm to minimize the dependence of RSS from the tilting angle among the nodes of a

localization sensor network.

1 INTRODUCTION

In recent years, the study of localization problems

have gained both commercial and academic interest.

Indeed, the direct use of a navigation system is today

a daily experience using smartphones, tablets and

car navigation systems.

Localize an object or a person means to link

them to a point on a map. In order to perform this

connection the distances of the object from reference

points have to be known. The localization

algorithms can be distinguished in outdoor and

indoor localization if the localized object or person

is mapped outside or inside a building respectively

(Franceschini et al., 2009, Curran et al., 2011).

The outdoor localizations are generally based on

satellite technologies: the most famous is probably

the Global Positioning System (GPS) (Hofmann-

Wellenhof et al., 1993) developed by the United

States Department of Defense, but also other

countries have developed similar technologies. For

example, European Union has recently started to

realize its own positioning system called GALILEO

again based on satellite signals (Hein et al., 2000).

Unfortunately the satellite technologies are limited

to free space, since satellite signals cannot generally

cross building walls. However, beside the satellite

technologies, in the last years, thanks to the

increasing spread of smartphones, diverse

applications based on mobile phone signals have

become accessible.

Indoor localization is generally based on radio

wireless technologies, even if localization system

based on Infrared and Ultrasound technologies have

been also developed (Randell et al., 2001).

However, also if different radio signal features or

technologies can be used, and different

environments can be taken into consideration,

triangulation methods are almost the only way to

estimate the object position. In particular, the

triangulation algorithms can be classified in

lateration and angulation algorithms (Hightower et

al. 2001). Lateration algorithms use the distances

among the object and at least three non-collinear

reference anchors to evaluate the relative position.

On the other hand, angulation algorithms use three

angle measurements performed on two anchors at a

known distance to obtain an estimation of the

relative position of the object. The angles have to be

measured in respect to the same reference, for

example the magnetic North. Generally, angulation

algorithms need more complex hardware than

263

Polese D., Pazzini L., Minotti A., Maiolo L. and Pecora A..

Compensation of the Antenna Polarization Misalignment in the RSSI Estimation.

DOI: 10.5220/0004810202630267

In Proceedings of the 3rd International Conference on Sensor Networks (SENSORNETS-2014), pages 263-267

ISBN: 978-989-758-001-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

lateration ones to measure the angle of seeing

between anchors and nodes, for example an array of

antennas. Lateration algorithms, instead, need

simpler hardware to evaluate the distance among the

nodes, since it can be based on propagation time

and/or signal strength. In particular, Time of Arrival

(TOA), Time Difference of Arrival (TDOA) and

Received Signal Strength (RSS) are well known

signal parameters used in indoor localization (Liu et

al., 2007, Patwari et al., 2005). Among these,

methods based on the RSS value have gained

growing interest, since RSS is probably the most

accessible transmission parameter that can be used

to estimate the distance between two nodes.

Furthermore, to measure the RSS, the more recent

RF transceivers have an integrated module that

estimates the RSS, the Received Signal Strength

Indication (RSSI). ZigBee protocol 802.15.4-2012

uses RSSI as indication data standardizing also the

RSSI measurement (IEEE 802.15.4f™-2012).

Moreover, its large use is also justified by the simple

equation that connect RSSI with distance (Aamodt,

2011):

10 log

SSI n d A   

(1)

Where n is the signal propagation constant, d is the

distance between sender and receiver and A is the

RSSI at a distance of one meter. n and A are two

parameters that depend on the medium and also by

the angle between the antennas. The easy use of

RSSI value in indoor environment is traded off with

several error factors as multipath, presence of

barriers between source and receiving antenna, angle

among the antennas.

Even if omnidiretional antennas are used, little

changing of angle between the antennas could give

large variation of the RSSI value and then larger

error on the measurement of the distance. These

variations are produced by the not ideality of the

omnidiretional antennas and by the misalignment of

polarization angle.

Previous works have shown the importance of

these problems in the power transmission. In

particular, Wadhwa et al. (Wadhwa et al., 2009)

have deeply investigated how the polarization

misalignment reduces the power transmission of a

wireless sensor network and they propose an

algorithm to estimate the relative antenna orientation

and find the low power transmission path. Whereas,

Huang (Huang, 2009) has shown that it is possible to

improve the localization performance also in

presence of antenna polarization losses with a better

estimation of the parameter n and A of equation 1.

Instead of reducing the effects of the polarization

losses in the RSSI estimation, in this paper, we

estimate the tilting angle between a static anchor

antenna and a moving antenna by means of an

accelerometer and then we use it to correct the

measured RSSI value. In this way, improving the

accuracy on the RSSI measurements it is possible to

improve also the accuracy on the anchor to mobile

node distance measurements (see equation 1), thus

enhancing the localization algorithms based on

lateration of RSSI signals.

The remainder of this paper is organized as

follows: in section 2 the algorithm of compensation

is presented. In section 3 the experimental set-up and

the results are shown. Finally, section 4 concludes

the paper.

2 RSSI LOSS COMPENSATION

The transmitted power between a transmit and

receive antenna depends also on the polarization

direction and spatial orientation. In particular,

misalignment polarization angle between antennas is

a well-known power loss factor that, for linearly

polarized antenna, can be easily estimated. Indeed,

knowing the relative misalignment angle between

the antennas (



, the polarization mismatch loss can

be evaluate using the following equation (Kishk,

2009) :

_ 20 log(cos )[ ]

SSI LOSS dBm





(2)

Therefore, the real RSSI, i.e. the RSSI that should be

measured without misalignment error can be

estimated using the following relation:

SSI RSSI RSSI LOSS





(3)

where

SSI

is the effective RSSI and

SSI

is the

measured RSSI. It is also important to point out that

the RSSI_LOSS value is always negative.

In order to estimate the polarization angle

between two antennas an inclinometer can be used.

In presence of movements where the acceleration is

much lesser than gravity acceleration, accelerometer

can be used as an inclinometer sensor (Luczak et al.,

2006). Inclinometer sensors based on accelerometers

use the known direction of the gravity acceleration

and the direction of the current gravity acceleration

in its local reference system to estimate the tilting

angle. Using this sensor it is possible to evaluate the

tilting angle respect the vertical axis.

Figure 1 shows a schematic representation of the

accelerometer, the three axis are highlighted by the

reference system.

SENSORNETS2014-InternationalConferenceonSensorNetworks

264

Figure 1: In figure a tri-axial accelerometer is shown.

Placing an inclinometer on a mobile antenna is

possible to estimate the orientation of the antenna

respect to the vertical, thus evaluating the angle

between the vertical axis and the antenna axis.

Furthermore, knowing also the anchor antenna

orientation respect to the vertical it is possible to

estimate the relative orientation of the antennas.

3 EXPERIMENTAL RESULTS

In order to verify the algorithm, two commercial

wireless sensor nodes Z1 Zolertia are used (Zolertia

Z1). Each node is equipped with an external

omnidirectional pigtail antenna (see figure 2). In this

configuration, one wireless node is used as an

anchor node, while, the other wireless node is used

as a moving node.

Several measurements of the RSSI have been

performed at different distances and several tilting

angles. During the measurements the mobile node is

firmly bound to the head of a tripod that can be

easily tilted in each direction (see Figure 3). At the

same time the anchor node continuously acquires the

data packets sent from the mobile node. The data

packet has a payload composed by the three axial

components of the acceleration. The RSSI value is

measured by the anchor node according to CC2420

specification (CC2420). Each measurement is

performed tilting continuously several times the

mobile node around two perpendicular directions as

figure 3 shows. Finally, acceleration and RSSI

values are sent to PC in order to perform the

following compensation algorithm.

The effective RSSI value is calculated using the

equation 2, but limiting the maximum loss value to

RSSI RSSI

in order to take into consideration

the non ideality of the system, i.e. the transmitted

power between two orthogonal linearly polarized

antennas is not zero. For each distance three

repetitions are performed.

In order to evaluate the goodness of the

compensation,

the standard deviation of the raw and

Figure 2: The two wireless sensor nodes equipped with an

external antenna.

Figure 3: The figure shows the mote firmly bound on the

head of the tripod. The two arrows show the two

orthogonal rotations performed during the measurements.

corrected RSSI are compared. Moreover, the

improving of the RSSI estimation is evaluated by

means of the following standard deviation ratio:

Σ

















(3)

Greater ratio means a better precision of the

measurements. Table 1 shows, for each distance, the

mean and standard deviation of the measured and

corrected RSSI. In particular, the parameter shows

that using the compensation algorithm it is possible

to increase the precision of the RSSI estimation i.e.

to reduce the standard deviation of the

measurements.

CompensationoftheAntennaPolarizationMisalignmentintheRSSIEstimation

265

Table 1: In this table the mean values and standard deviation of the RSSI for several distances measurements are reported.

RSSI

is the measured RSSI value, RSSI

is the effective RSSI. μ(·) is the mean and σ(·) is the standard deviation. For each

distance, the standard deviations among the different measurement repetitions are also reported.

Distance [m] 1 2 4 6



RSSI

)[dBm]

2.5931  0.0918 3.9685  0.9025 10.3634  0.2591 13.9524  0.2384





RSSI

)[dBm]

5.8428  0.3043 7.0881  0.1037 5.3859  0.3793 4.8319  0.5009



RSSI

)[dBm]

6.4558  0.1994 0.0826

 0.9147

5.9072  0.6053 10.0582  0.1136





RSSI

)[dBm]

4.5586  0.4693 5.4023  0.2295 5.2761  0.2435 4.1028  0.6833

Σ

1.2817 1.3121 1.0208 1.1777

Figure 4: In the figure the mean profile of the measured

RSSI is compared with the mean profile of the

compensated RSSI during several tilting. The measured

RSSI changes with the cosine of the tilting angle whereas

the compensated RSSI is almost unchanged.

Figure 4 shows an example of a RSSI measurement

when the mobile node is tilted several times. The

measured RSSI changes with the cosine tilting angle

whereas the compensated RSSI is almost unchanged,

this means that the algorithm is able to reduce the

losses due to the antenna misalignment error.

Figure 5 shows another evidence of better

evaluation of the RSSI using the proposed

algorithm. In this figure, the accuracy of the

estimated RSSI value is evaluated sketching the

differences among the RSSI.

measured when the polarization effect is negligible

(cos( ) 0.99)





, thereafter called RSSI aligned (

RSSI

), and the mean value of the corrected and

raw RSSI.

RSSI

can be considered the correct

RSSI value, since it is the RSSI value measured

when the antennas are aligned. The average values

of the RSSI corrected and raw are performed on the

whole set of the acquired values during the

measurement. In particular, the RSSI is evaluated in

an almost continuous range of angle values between

approximately 0 to  rad.

02468101214

|RSSI

- RSSI

|RSSI

- RSSI

RSSI error [dBm]

Measure number

Figure 5: Absolute difference between the RSSI measured

with zero angle of tilting and the mean value of the RSSI

measured and corrected for all the rotations. Correcting

the RSSI it is possible to reduce the mean error in the

RSSI evaluation of almost 2 times.

Values near to zero of the previous difference

(







) mean that the compensation

algorithm is able to correctly counterbalance the

power loss effect depending on the antenna

misalignment. It is worth of noting that this

difference is always lesser than the difference with

the raw values (

RSSI RSSI

). Moreover, for the

most part of the measurements this difference is

lesser than one dBm whereas uncorrected difference

is always greater than three dBm. These results

suggest that this algorithm can potentially minimize

the antenna misalignment errors.

4 CONCLUSIONS

In conclusion, a simple algorithm to reduce the

effect of the tilting angle between antennas has been

0 0.2 0.4 0.6 0.8 1

-15

-10

-5

Cosine of tilting angle

RSSI [dBm]

Measured RSSI

Compensated RSSI

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266

shown. A common accelerometer utilized as tilting

sensor is exploited to estimate the RSSI loss that is

used to correct the measured RSSI. In this way, the

proposed algorithm can evaluate the misalignment

angle between two antennas in order to compensate

the transmission losses.

The algorithm has been tested using two

commercial Wireless Sensor Nodes and the results

have shown that it is able to reduce the dependence

on the tilting angle of the RSSI at least by a factor 2.

ACKNOWLEDGEMENTS

This research was partially supported by the

Flagship Project "Factory of the Future"

FACTOTHUMS of the National Research Council.

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