Dott. Reetu Singh RELAZIONE SULL'ATTIVITA' E LE RICERCHE ...

Dott. Reetu Singh
RELAZIONE SULL'ATTIVITA' E LE RICERCHE SVOLTE ALLA CONCLUSIONE DEL
3° ANNO DEL XVIII CICLO DEL CORSO DI DOTTORATO DI RICERCA IN SCIENZE
E TECNOLOGIE DELL’INFORMAZIONE E DELLA COMUNICAZIONE, INDIRIZZO:
SCIENZE ED INGEGNERIA DELLO SPAZIO
1. TEMATICHE DI RICERCA
Le tematiche da me sviluppate in questo anno hanno avuto per oggetto
1. Sviluppo e implementazione di un algoritmo per le localizzazione in outdoor basata su WLAN.
2. Sviluppo e implementazione di un algoritmo per le fusione dati tra sistemi di posizionamento
WLAN e Videocamera.
3. Sviluppo di metodologie efficiente per il miglioramento delle termine di posizionamento indoor
di un sistema WLAN.
1.1 Sviluppo e implementazione di un algoritmo per le localizzazione outdoor basata su WLAN.
Location information is of paramount importance in context aware Ambient Intelligence (AmI), Smart Space, traffic
monitoring, surveillance network and cooperative communications services. This part describes a Positioning
determination solution based on wireless local area network (WLAN) signals. Position determination is based on the
statistical modeling of the received signal at any position. This paper presents a probabilistic based statistical modelling
approach for location estimation which incorporates fusion strategy in final step to combine efficiently the location
individually reported by each WLAN transmitter [7] [9][10]. The system builds a radio map of the environment. The
presented system is easier to implement and provide sufficiently good performance under all conditions. The accuracy
with the 90% probability is reported to be 3.6 meters
meters where as average error is reported to be 4 meters.
Basically, the positioning has been indigenously put apart into two parts, Indoors and Outdoors [1]. Since there are
many ways to categorize the positioning systems, in this paper we will just refer to indoors positioning systems based
on WLAN. This part presents a probabilistic based statistical modelling approach for location estimation which
incorporates fusion strategy in final step to combine efficiently the location individually reported by each WLAN
transmitter. The system builds a radio map of the environment. The concept of radio map is based on collection of
signal strength over a set of strategically selected coordinates in a known referenced environment. The set of signal
strengths collected over known positions is treated as its signature. The issues related to duration of time in which the
signal strength should be collected is still needed to be cleared. However on average, in the current state of art, typical
time spent on collecting survey data set is 5 minutes with a sampling period of 1 second . Most of the indoor positioning
systems are based on the radio map. The presented system is easier to implement and provide sufficiently good
performance.
The experiment site created to test this method is described here in. Experiments results on outdoor can be found below:
Experiment site
The 2D map of the outdoor experiment test bed depicts the outdoor experiment test bed. Around six transmitter have
been installed as indicated in green rectangle. The Wi-Fi are compliant with the 802.11b standard specifically cisco
1100 series. The Cisco Aironet® 1100 Series delivers an affordable and up-gradable 802.11b wireless LAN (WLAN)
solution. The Cisco Aironet 1100 Series wlans integrates seamlessly to form a wireless networks. Cisco 1100 series
covers 490 ft (150 m) @ 11 Mbps outdoor and 220 ft (67 m) @ 11 Mbps indoors. The maximum transmitter power
available is -100 mW (20 dBm) and it has a sensitivity of -85 dBm at-11 Mbps. WLAN operate in the ISM band of 2.4
Ghz have omni-directional antenna and based on IEEE 802.11b. During experiment mobility was not enabled and data
transfer rate was set to 11Mbps. The transmitter was set to transmit at 100 mw. This test bed was set up outdoor of
building department. The infrastructure of Wireless Local Area network in my department (D.I.B.E, University of
Genova, Italy) covers the area of 2400 square meters. As seen from figure, whole outdoor area is grid into square size of
meters. The origin is indicated on the Figure. Six AP's have been installed at the positions (27.5 2.5), (11 -3), (14 5), (16
1), (3 1) and (16 2) for AP1, AP2, AP3, AP4, AP5 and AP6 respectively. Each WLAN has been assigned individual
SSID. There are approximately 120 grid points marked for calibration. These grid points are those survey points where
received signal strength measurements was taken during calibration/offline (depends upon algorithm) phase. Received
signal strength measurement (RSSm) is collected discreetly in time. A wireless sniffer open source software

WIRELESS TOOLS is modified and developed for signal acquisition and power measurements. The software (RSS
extractor) runs on Linux platform installed on my laptop (Pentium IV) and forms a server for online location estimation.
When RSS extractor software is executed, it launches wireless tools sniffer and keep on collecting RSS samples
measurements with specified sampling rate. A sampling rate can be adjusted automatically. The program developed to
collect RSS samples (collect RSS) during offline phase runs for specified number of times with desirable delay. In our
experiment we fixed number of samples to be 300 and delay = 1s. The code can be found here. While online RSS
EXTRACTOR runs continuously in time simultaneously computing positions.
Fig. 1 2D map of the outdoor experiment test bed comprising WLAN infrastructure
Results:
The algorithms is implemented in Matlab 6.5 environment. The developed method was tested on the experimental data.
Test data was collected at various location, throughout the test site. First at the same position measurements was
collected and position algorithm was used to computes user’s location. The computed position was compared with the
real position by mean of Euclidean distance that gives the shortest distance between real and computed position. The
probability of obtaining error for outdoor is shown and also compared with indoor result, shown in figure 2a. The
average error comes out to be 4 meters. More elaborate experiment was done to find out how positioning error varies in
space. For this purpose set of location coordinates distributed in space was chosen and measurements was collected on
every position. The error obtained for these location is shown in figure 2b.
RMSE error
8
7
6
5
4
3
2
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Discreet Positions
2(a) 2(b)
Fig 2. RMSE error obtained on each positions for outdoor
1.2 Sviluppo e implementazione di un algoritmo per le fusione dati tra sistemi di posizionamento
WLAN e Videocamera.
In this part the issues related to the association between heterogeneous sensor has been mainly considered. Two
homogeneous sensors are taken to be WLAN and VideoCamera. Basically second step of fusing two given sensor,
once they have been aligned, lies in associating the tracks obtained from video and radio sensor. In the considered

scenario, video sensor produces a more defined and accurate positioning information. Track obtained by this sensor is
more similar to the real path taken by object. On the other hand, position obtained by WLAN sensor are more affected
by the multi-path in indoor. The positioning error is space variant. Moreover, positioning error increases when a person
moves closer to wall. As a result, the tracks obtained by WLAN sensor are not uniform and they show high error with
respect to original objects path. However, the shape of the track does not deteriorate. This problem can also be treated
as a problem of pattern matching. The matching method allows one to decide if a given set of variables belongs to same
class or not. For this reason, the Euclidean based technique has been utilized.
The Euclidean distance D E is defined as :
T
( P ( k ), P ( k )) = ( P ( k ) − P ( k )) ( P ( k ) − P ( k ))
DE r v
r v
r v
P r and P v is a set of n samples coming from video sensor and radio sensor, respectively. The decision is taken
based on the distances obtained for each pair of sequences obtained by both set of samples at every time stamp. Since
WLAN positioning error are space varying, this a priori information may also be incorporated during decision
making.
In Figure 3(a) the two objects are shown to be present in test site during experiment. The real path taken by these
objects inside room is marked with the dashed line. The object tried to maintain speed throughout. During the
experiment, both object were holding Laptop supporting WLAN receiver 802.11b IEEE. The software installed on the
devices could scan the RSS from all three access points at one scan per second sampling rate. While recording the
signal strength it was seen that object one is receiving highly attenuated signal. This could be either due to being closer
to the wall receiver’s sensitivity or hardware thermal noise present in his receiver. However, this problem needs to be
dealt separately.
Euclidean distances D E are calculated for each position coordinates obtained on synchronized time stamp. The
distances between these coordinates are compared with their relative covariance value, κ . These covariance values are
learned from test site on collected measurements. The output is quantized into two levels: 1 when distance is less than
their relative covariance and 0 otherwise.
⎧1,
= ⎨
⎩0,
d < κ
d > κ
D E (11)
After iterating this procedure on available set of position coordinates, the decision was taken based on the number of
times 1 and 0 was occurred. The tracks are said to be matching if number of occurrence of 1 is greater than number of
occurrence of 0 and non-occurring in other case.
Below, few considered scenarios have been considered:
Y axis
Origin
Object 1
Object 2
X axis
Figure 3 (a). The real path taken by objects in test site Figure 3(b). The experiment test site
Results:
The association between tracks are truly dependent upon individual performances and their consistency. Figure 4 shows
the distances between set of positions reported by individual sensors, object 1 and object 2. The results are promising
however intensive research still requires to stabilize the system when number of user increases in the presented
environment. The problem is not yet exhausted. It is possible to improve the produced result by making each class of
system more robust, individually.

Euclidean distance
Euclidean distance
8
6
4
2
(a) Object 1
0
0 10 20 30 40 50 60
Discreet position at each k time stamp
(b) Object 2
8
6
4
2
0
0 10 20 30 40 50 60 70 80
Discreet position at each k time stamp
Figure 4. The distance been positions obtained by radio and video objects at different continuously varying time stamp.
The mean distance between radio and video object 1 is 3.9 meters while 2.2 meters for object 2.
1.3. Sviluppo di metodologie efficiente per il miglioramento delle termine di posizionamento
indoor di un sistema WLAN.
Simple classical triangulation based positioning techniques generally fails in indoor scenarios [1]. The complex signal
propagation in indoor introduces enough multipath error and noise to generate error in positioning [2][3][4]. Therefore,
a model-based signal distribution scheme is adopted to learn the environment [5]. A simple propagation path loss model
as described in [4] is considered. Multipath effect is shown using a random variable which is considered to be gaussian
distributed ξ, as shown below
Pr(d) = Po − 10αlg(d) − ξ (1)
where Pr(d) is the received signal strength at d meter and d is the distance between transmitter and receiver. Po is the
received power at one meter distance and α is known as the path-loss exponent. The system works in two phases:
offline and online.
Offline: During offline phase the survey data set consisting of RSS are collected on a known prefixed locations. These
information are utilized to calibrate the system. A method proposed in [5] is adopted to built the signal distribution
model named as Feature Function. It is model based signal distribution learning scheme. This is a method to
compensate for the multi-path fading and noise in the received signal by empirical analysis of the propagating signal in
the environment. Feature Function is a function that describes the distribution of the parameter, which is computed by
taking the ratio
of noise power to observed received signal power, on a corresponding distances. This function is constructed by
incorporating the measurements taken on calibrated locations. Assuming the ideal propagation condition and a fixed
transmitted power, the ideal received signal strength is simulated on all known calibrating locations, where path loss
exponents are computed using the same survey data set. A noise, i.e a difference between synthesized and observed
RSS, is generated on all calibrating location point. RSS measurements observed on these locations are compared with
synthesized one and a parameter is created. This parameter is obtained by simply taking a ratio of the noise and
observed RSS measurements values.
Online: The Feature Function is stored radio map of the test site. Receiver collecting set of signal strength from all
access points can use FF to correct estimated distances. The corrected distances is used in position algorithm which
returns 2D estimated coordinates. These coordinates are with reference to positions of access points. The signal
distribution model developed in offline phase returns a parameter that represents the error associated with each
observations. This parameter is employed to do correction in the observed signal strength. The corrected signal strength
is utilized to estimate the distances and thus compute positions.
Results: Results obtained by improved geometrical methods shows a root mean square error (RMS) error of 2.6 meters
with 90% probability, respectively. Both positioning algorithms was tested on set of measurements obtained in different
days and time. The result can be seen in Figure 5. It is interesting to see the different in positioning error on fifteen
location obtained in different time and days.

References :
Fig. 5. Positioning error distributed in time and space in case of Geometrical based Positioning method
[1] P. Enge, and P. Misra, ”Special issue on GPS: The Global Positioning System,” Proc. of the IEEE, pp. 3-172, Jan.
1999.
[2] Parkinson and Spilkar, ”Global Positioning System: Theory and Application 1” Eds./Axelrad and Enge, Progress in
Astronautics and Aeronautics, Volume 163.
[3] J. Hightower and G. Borriello, ”Location Systems for Ubiquitous Computing,” IEEE Computer, pp. 57-66, IRS-TR-
01-
001, Aug. 1, 2001.
[4] J. Caffery Jr., G. Stuber, ”Overview of Radiolocation in CDMA Cellular Systems”, IEEE Comm. Mag, April 1998,
Vol. 36, Isuue 4, Page(s): 38-45.
[5] Reetu Singh, Matteo Gandetto, Marco Guainazzo, Daniele Angiati, Carlo S. Regazzoni, ”A novel positioning
system for static location estimation employing WLAN in indoor environmtn,” IEEE PIMRC 2004, Stockholm.
[6] Reetu Singh, Luca Macchi, K.N. Plataniotis, C. S. Regazzoni, ”A Statistical modelling based location determination
method using fusion technique in WLAN” IEEE, IWWAN 2005, London May 23-26.
[8] K. Pahlavan and P. Krishnamurthy-Principles ofWireless Networks: A Unified Approach - Prentice Hall PTR,2002.
[9] T. S. Rappaport - Wireless Communications: Principles and Practice - Prentice-Hall Inc., New Jersey, 1996.
[10] M.H. DeGroot - Probabilistic and Statistics - second ed.
2. ELENCO DELLE PUBBLICAZIONI (dall'inizio dell'attività di ricerca)
1. R. Singh, L. Macchi, C.S. Regazzoni, “A Statistical Modeling Versus Geometrical Location Determination
Approach for Static Positioning in Indoor Environment”, WPMC, IEEE, September 17-22, 2005, Aalborg,
Denmark.
2. Maristella Musso, Matteo Gandetto, Gianluca Gera, S. Canepa, Reetu Singh, Carlo Regazzoni, “ A novel
combined algorithm for 32-QAM carrier recovery” WPMC, IEEE, September 17-22, 2005, Aalborg,
Denmark.
3. R. Singh, C. S. Regazzoni, “Statistical Modeling based probabilistic method for Position estimation in Indoor
Environment”, IEEE, International workshop on Wireless Ad-hoc Networks (IWWAN) , 23-26 May, 2005,
London.
4. R. Singh, M. Gandetto, M. Guainazzo, D. Angiati, C. S. Regazzoni, “A novel positioning System for static
location estimation employing WLAN in indoor environment”, PIMRC 2004, IEEE, Barcelona, Spain, 5-8
September 2004.
5. R. Singh, M. Gandetto, M. Guainazzo, L. Macchi, C.S. Regazzoni “A novel method for static and dynamic
location sensing employing WLAN network infrastructure”, Wireless Personal Multimedia Communications,
WPMC 2004, IEEE, Abano Terme, Italy, 12-15 September 2004.

...
6. L. Marchesotti, R. Singh, C.S. Regazzoni, “Extraction of aligned video and radio information for identity and
location estimation in surveillance systems”, The 7th International Conference on Information Fusion,
Stockholm, Sweden, 28 june-1 July 2004.
7. R. Singh, M. Guainazzo, C. S. Regazzoni,”Location determination using WLAN system in conjunction with
Global Positioning System” VTC IEEE 12-15 May 2004, Milan, Italy.
8. S. Piva, R. Singh, M. Gandetto, C.S: Regazzoni, "Video and Radio Attributes Extraction for Heterogeneous
Location Estimation - A Context-based Ambient Intelligence Architecture" in P. Remagnino, G.L. Foresti T.
Ellis eds., Ambient Intelligence, A Novel Paradigm, Springer, USA, ISBN 0-387-22990-6, 2005, pp. 63-87.
9. C. S. Regazzoni, R. Singh, S. Piva, "Intelligent fusion of visual, radio and heterogeneous embedded sensors'
information within cooperative and distributed smart spaces", to appear in book "Data Fusion for Situation
Monitoring, Incident Detection, Alert and Response Management", NATO Science Series, Kluwer Academic
Publishers, 2005 (in press) .
10. S. Piva, R. Singh, M. Gandetto, C.S: Regazzoni, "Heterogenious sensors data fusion issues for harbour
security" NATO ARW 2005, Data Fusion Technologies for Harbour Protection, Tallinn, Estonia (June 27 -
July 1) (in press).
11. Carlo Regazzoni, Reetu Singh, Stefano Piva, "Integration of Visual, Radio and Heterogeneous Sensor
Information within Cooperative and Distributed Smart Spaces," School NATO 2003, Armenia.
12. Fabio Lavagetto, Carlo Bonamico, Alessandro Iscra, Paolo Pisano, Carlo Regazzoni, Stefano Piva, Andrea
Cattoni, Reetu Singh, "Document 1: Scene Analyses and the performance criteria for evaluating Localization
technology", Technical Report of “Studio sull’impiego integrato di techniche multimodale per la
localizzazione ed il tracking di mezzi mobile e persone in ambiente indoor e outdoor”, finanziato dal Parco
Scientifico e Tecnologico della Regione Liguria, Genova, 31/01/05 (In Italian).
13. Fabio Lavagetto, Carlo Bonamico, Alessandro Iscra, Paolo Pisano, Carlo Regazzoni, Stefano Piva, Andrea
Cattoni, Reetu Singh, "Documento 2: Analysis of state of art technology and innovative multimodal
localization and tracking techniques application to mobile or stationary object in outdoor or indoor
environment", Technical Report dello “Studio sull’impiego integrato di techniche multimodali per la
localizzazione ed il tracking di mezzi mobile e persone in ambiente indoor e outdoor”, finanziato dal Parco
Scientifico e Tecnologico della Regione Liguria, Genova, 31/10/05 (In Italian).
14. R. Singh, M. Musso, G. Gera, C. S. Regazzoni, “User centric location method based on WLAN Signal
strength(RSS) measurements and tri-lateration positioning” Technical Activity Report, , WP3-II year Section
3.1.3, VICOM Project, 22/12/2004.
15. R. Singh, M. Musso, G. Gera, C. S. Regazzoni, “User centric location method based on WLAN Signal
strength(RSS) measurements and tri-lateration positioning” Technical Activity Report, WP3-I year VICOM
Project, 17/11/2003.
16. R. Singh, C.S. Regazzoni, Kostas Plataniotis, “An enhanced WLAN-based positioning systems for indoor a
pplications”, Wireless Communications and Mobile Computing, Wiley Interscience Journal, (Submitted).
17. R. Singh, M. Musso, C. S. Regazzoni, “Statistical Modelling based location estimation in WLAN environment:
an indoor/outdoor”, Signal processing letters, IEEE (Submiting).
18. M. Musso, R. Singh, G. Gera, C. S. Regazzoni, “Enhanced Code Tracking Design for a BOC modulated
GALILEO Signal”, The journal of Global positioning system (Submiting).
3. PIANO DI STUDI (degli anni completati)
3.1. Corsi di "Sistemi di Telecomunicazione 1" Prof. Carlo Ragazzoni.
3.2. Corsi di "Distributed detection and data fusion " Prof.P. Varshney.
3.3. Corsi di "Anaysis di Fourier " Prof.D. Mari.
3.4. Corsi di "Analasis di Functionali " Prof. Zollezi.
3.5. Corsi di "Distributed detection and data fusion " Prof.K. Plataniotis.
3.6. Corsi di “Gestione delle Imprese High Tech”, Ing. Cuneo Giuseppe.
3.7. Corsi di "Tecniche e sistemi di transmissione fissi e mobili" Prof. Carlo Regazzoni.

3.8. Corsi di “Servizi telematichi multimediale distribuiti in applicazione per intelligenza di
ambiente ”, Prof. Carlo Ragazzoni.
(con riferimento al corso o ai corsi sostenuti alla fine di ogni anno, dovete allegare la
dichiarazione, rilasciata dal docente interessato, di superamento dell’esame o degli esami (vedi
All.1); per le scuole o cicli di seminari, dovete allegare la certificazione di frequenza e di eventuali
esami finali)
4. PARTECIPAZIONE A SCUOLE, CORSI, ecc. (eventuale; non riportare la partecipazione a
conferenze, convegni ecc.)
1. Partecipazione in "La Stazione Spaziale Internazionale: un programma tecnologico di
collaborazione intenazionale" Agenzia Spaziale Europea (ESA), Prof. S.Cincottii
5. PERIODI DI FORMAZIONE SVOLTI ALL'ESTERO (eventuale)
2. Four weeks of study period at the University of Toronto, Canada.