map-based location and tracking in

814 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 10, NO. 3, MARCH 2011

Map-Based Location and Tracking inMultipath Outdoor Mobile Networks

Marco Anisetti, Claudio A. Ardagna, Valerio Bellandi, Ernesto Damiani, Senior Member, IEEE,and Salvatore Reale

Abstract—Technical enhancements of mobile technologies arepaving the way to the definition of high-quality and accurategeolocation solutions based on data collected and managedby GSM/3G networks. We present a technique that providesgeolocation and mobility prediction both at network and servicelevel, does not require any change to the existing mobile networkinfrastructure, and is entirely performed on the mobile networkside, making it more robust than other positioning systems withrespect to location spoofing and other terminal-based securitythreats. Our approach is based on a novel database correlationtechnique over Received Signal Strength Indication (RSSI) data,and provides a geolocation and tracking technique based onadvanced map- and mobility-based filtering. The performanceof the geolocation algorithm has been carefully validated by anextensive experimentation, carried out on real data collected fromthe mobile network antennas of a complex urban environment.

Index Terms—Geolocation, GSM, 3G mobile communicationnetworks, Kalman filters, received signal strength indication.

I. INTRODUCTION

THE increasing diffusion of mobile devices and the cor-responding improvements of sensing technologies make

available a great amount of accurate location information,which can be used to provide reliable, accurate, and precisealgorithms for computing the position of users carrying mobilephones. The term “geolocation” denotes a variety of tech-niques aimed at mobility prediction, that is, computing andtracking the position of mobile terminals, and refers to one ofthe hottest topics in wireless and mobile computing research.Mobility prediction [1], [2] can be used both at networklevel to support several crucial tasks for network management(e.g., handoff management [3], efficient code division in 3Gnetworks [4]) and at service level to support𝑚-commerce anda number of Location-Based Services (LBSs) (e.g., navigationservices, emergency rescue [5]). In both contexts, the geolo-cation precision and accuracy play a fundamental role.

Geolocation of mobile terminals has been extensively con-sidered in past works. Early research considered locationtechniques based on time measurements, such as, Time ofArrival (ToA), Time Difference of Arrival (TDoA), Enhanced-Observed Time Difference (E-OTD) [6], [7]. Time-based meth-ods are precise only when the Line-Of-Sight (LOS) between

Manuscript received January 8, 2010; revised October 12, 2010; acceptedDecember 10, 2010. The associate editor coordinating the review of this paperand approving it for publication was T. Hou.

M. Anisetti, C. A. Ardagna, V. Bellandi, and E. Damiani are with theDipartimento di Tecnologie dell’Informazione, Universita degli Studi diMilano, Italy (e-mail: [email protected]).

S. Reale is with GREENGEGNERIA.IT, Italy (e-mail:[email protected]).

Digital Object Identifier 10.1109/TWC.2011.011811.100025

the mobile terminal and the base stations is completely free ofobstacles [6]. In urban environments, where scenarios with NoLOS (NLOS) are common, precision of time-based methodsis usually poor. Another major limitation of these techniquesis that the accuracy of the estimated position heavily dependson the number of measurements and on the placement of theantennas, which must support triangulation. Other solutionsfocused on satellite-based positioning systems, such as thepopular Global Positioning System (GPS) [8], [9]. StandardGPS geolocation performs rather poorly in dense urban areasor inside buildings, where satellites are not visible by mobileterminals, and makes the location information available onthe mobile device side only [8]. Recent enhancements inGPS technology resulted in the definition of Differential GPS(DGPS) [8] and GPS with Real Time Kinematik (RTK) [10],high-accurate positioning systems that achieve a precisionless than a meter at the price of adding ad-hoc signals forerror correction. These systems however still suffer of thesame problems of GPS in urban environments [8]. For thesereasons, we claim that even leaving their cost and impact onbattery consumption aside, satellite-based techniques are notlikely to be the key technology for a number of interestingLBSs, such as the identification and certification of mobileterminal trajectories. Another class of approaches relies onWLAN positioning techniques (e.g., [11], [12]). For instance,Skyhook’s WiFi Positioning System (WPS) [12], originallyadopted by Apple for the first generation of its iPhone andiPod-touch devices, uses known locations of wireless accesspoints exploiting mobile terminal ability to detect such accesspoints identity, even when they do not join the correspondingwireless network. WPS techniques put the mobile terminal incharge of its own localization, but assume WiFi access pointsto be trustworthy. Experimental work at ETH Zurich [13]has shown the vulnerability of Skyhook’s and similar publicWLAN positioning systems to location spoofing attacks. Otherapproaches [14], [15] proposed to identify signal propertiesthat support precise geolocation of mobile terminals, and tomodify the network infrastructure to provide such signals.In urban and indoor environments, however, signal-basedsolutions are affected by the multipath signal problem [16]:incoming signals are reflected by obstacles, and may arriveat destination in multiple copies, following different paths.The presence of very close obstacles makes filtering multiplecopies of incoming signals difficult, as the time differencebetween each reflected copy is very hard to predict. Thisproblem can be alleviated by devising an ad-hoc, multi-carrier signal structure supporting spectrum-based detection ofcopies [17]. However, a generalized adoption of multicarrier

1536-1276/11$25.00 c⃝ 2011 IEEE

ANISETTI et al.: MAP-BASED LOCATION AND TRACKING IN MULTIPATH OUTDOOR MOBILE NETWORKS 815

geolocation signals would require substantial modificationsto the existing mobile network infrastructure, including aspecialized radio equipment for geolocation.

The line of research closest to the work presented inthis paper considers location techniques based on ReceivedSignal Strength Indication (RSSI) [18], which measures signalattenuation, assuming free space propagation of the signaland omnidirectional antennas. Signal level contours around abase station are modeled as concentric circles, where smallercircles correspond to higher power values [18]. Similarly totime-based approaches, RSSI-based mobile terminal locationis reduced to the well-known triangulation position problem.In this paper, we present a solution based on RSSI and ondata normally collected and managed by GSM/3G networks.The main contribution of the paper is developing our previousresearch [1] towards the definition of a technique that: i)provides high-accurate geolocation and mobility predictionthat can be used both at network and service level to provideenhanced functionalities [19], ii) does not require any changeto the existing mobile network infrastructure, and iii) is per-formed at the mobile network side (network-centric), making itmore robust against location spoofing and other terminal-basedsecurity threats. More specifically, we first propose to applyan improved version of the traditional Database CorrelationMethod (DCM) [20] to incoming signal strengths to identifya set of candidate positions (Section II). We then describean Enhanced Time-Forwarding Tracking (ETFT) technique, asan evolution of our previous TFT [1], that exploits GIS mapinformation and a predicted motion model to produce a setof candidate paths (shadow paths in the following) that bettersuit the motion and map constraints (Section III). Our ETFTtechnique deals with signal fluctuations, building an extendedTime-Forwarding Graph (eTFG) that is used for an initialrough skim of candidate locations, and introduces a Time-Forwarding Filtering (TFF) to perform a high-accurate pathselection. Moreover an advanced map-based Kalman filteringis applied to the selected path in order to enforce other mapconstraints (Section IV). This additional filter exploits mapinformation about the roads to form a coherent path thatbetter approximates the actual mobility. Finally, to test thequality of our geolocation algorithm, we perform an extensiveexperimentation based on real data collected from the mobilenetwork antennas of a complex urban environment, usingdifferent devices, and over a long period of time (Section V).

II. DCM WITH MULTIPLE CANDIDATES

Electro-Magnetic Field (EMF) prediction is a crucial taskand an important source of error for geolocation techniquesrelying on triangulation or DCM over RSSI. In particular,four major phenomena influence the radio propagation andthe location precision: reflection, diffraction, penetration, andscattering. As a consequence, traditional RSSI location esti-mation is not well-suited to urban areas, lacks of precisiondue to multipath propagation and building shadowing, andis, in principle, as reliable as the one obtained by time-based approaches. Good modeling of electro-magnetic signalpropagation however has proven to be useful in mitigatingthe lack of precision of RSSI in urban environments. Ad-vanced deterministic models (ray-tracing, IRT [21]), empirical

approaches (Hata-Okumura [22], Walfisch-Ikegami [16]), andhybrid techniques (Dominant Path [23]) for EMF predictionhave been successfully developed for various environments.However, many problems remain to be solved, including man-aging the fluctuations introduced by environmental changes.An interesting approach to deal with EMF fluctuations usesvector regression [24]. In a nutshell, regression techniquesmodel the location problem as a checkpoint location, whichcan be solved as a machine learning problem. However,real data sampling are not always available and the needfor a training phase jeopardizes the practical applicability ofmany machine learning techniques. Furthermore, checkpointand neural network strategies are not suitable for trajectorytracking and dense localization.

In this paper, we adopt our EMF prediction algorithmin [1] that enhances the COST231 Walfisch-Ikegami [16]using GIS information (i.e., buildings’ elevation) and realantenna’s shapes. This approach makes statistic predictionmodel COST231 suitable for real environments, by reducingthe loss in the prediction quality introduced by omnidirectionalantennas. Although adding antennas’ shapes to the COST231model improves EMF prediction quality, this is still notenough to tackle all physical phenomena influencing the EMFprediction. In [1], we provided a comparison between the realRSSI of a terminal and the predicted one using antenna’sshapes and COST231, showing how the RSSI performanceis affected by fluctuation effects.

To reduce the effects of such phenomena, our geolocationsolution extends the database correlation approach proposedin [25] by using multiple candidates. In [25], the position ofa mobile terminal is determined by comparing signal strengthmeasurements performed by the mobile terminal itself with theEMF prediction stored in a lookup table. The lookup table is amatrix where every row represents a single point 𝑝 within thecoverage area (expressed in Cartesian 𝑥, 𝑦 coordinates, if 2DCartesian representation of the area is used, or as a latitude andlongitude pair, if GPS representation is used), every columnan antenna, and every entry the path loss prediction from anantenna to 𝑝. To keep computational complexity under control,lookup table filling and update can be done once in a while,when major changes in the area of interest occur. Generallyspeaking, computing the lookup table for a given area consistsin super-imposing a grid where field levels are quantized.This grid needs not to be uniform; rather, its sparseness canbe controlled on the basis of the characteristics of the areaof interest and cost constraints. The estimated location ofthe mobile terminal is defined as the entry in the lookuptable whose coordinates minimize the location error 𝑒. Thelocation error is calculated as the sum of the squared errorsbetween the path loss measured by the mobile terminal foreach antenna in the area of interest and the path loss predictedfor a given entry in the lookup table for the same set ofantennas.1 More in detail, given 𝑟 antennas in the area ofinterest, we denote 𝑀 [𝑗] as the path loss measured by themobile terminal from the antenna with index 𝑗 and 𝐸[𝑖,𝑗] thepredicted path loss from antenna 𝑗 to the 𝑖-th cell in the grid.

1In our solution, the path loss measured by the mobile terminal iscommunicated and elaborated by the network itself that is in charge of thewhole geolocation process.


(a)

(b)

Fig. 1. An example of a set of candidates for localization at two consecutivetimes t and t+1 in a real environment.

The location error 𝑒𝑖 is defined as∑

𝑗∈𝑟 (𝑀 [𝑗]− 𝐸[𝑖, 𝑗])2,where 𝑖 is the cell under consideration; the estimated locationof the mobile terminal is the cell 𝑐 in the grid such that𝑒𝑐=min𝑖

(∑𝑗∈𝑟 (𝑀 [𝑗]− 𝐸[𝑖, 𝑗])2).

Any single-point location technique however suffers of anintrinsic error due to discrepancy between real EMF strengthand the predicted one in the lookup table [1]. The maincauses of error in predicting EMF strength are: i) intrinsicmodel error, e.g., due to irregular skyline; ii) imprecision ingeographical database, e.g., due to information aging of themap; iii) variation in the antenna features, e.g., any kind ofpower or gain variation; iv) variation in weather conditions.This intrinsic error is also due to EMF fluctuations andis spread all over the area of interest. As a consequence,computing a single estimated location using squared errorminimization on a fixed number of stations hardly ever givesthe correct location. Often the possible candidates do not evenfall in a single region; rather, they are scattered all over thegeographical area. Fluctuation may make one or the other ofthese candidates the “best” one at any given time, regardlessof the distance between them.

Our approach improves over single-candidate selection byusing a variable number 𝑛 of position estimates [11]. Figure 1shows an example based on real network data. In the example,selection is performed at two consecutive times t and t+1.Multiple candidates are shown as points, the best single

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0 10 20 30 40 50 60

Error (m)

Number of Candidates

Fig. 2. Minimum local error as a function of the number of candidates.

candidate as a big point, and the real position as a smallcircle. From the example, it is clear that, using only the bestcandidate (big point), the location quality is greatly reducedby fluctuation effects. Multiple candidates, instead, give aconsiderable improvement in terms of quality, and make thesolution more robust against fluctuations. Also, the higher thecandidates number, the higher the probability of includinga location that better approximates the real position (seeSection III for more details). By contrast, multiple candidatesincrease the complexity of the best candidate selection processin Sections III and IV. A solution balancing precision andperformance is therefore needed. In our experiments (seeSection V), we consider the urban environment in the cityof Milan. Based on this environment, Fig. 2 shows the trendof the minimum local error (in meters) produced by the bestcandidate in the set (𝑦 axis) as a function of the maximumnumber 𝑛 of possible candidates (𝑥 axis). From the graph,it can be observed that the error decreases substantially until𝑛=20. For 𝑛 greater than 20, instead, the gain in terms ofprecision is trifling, while the complexity in calculating thebest candidate still increases. As a consequence, in urbanenvironments like the one considered in this paper, whereGIS information is available, 𝑛=20 represents a good value tobalance precision and performance and an upper bound thatfits well all types of environments. In general, the number 𝑛of candidates that represents the best compromise betweencomplexity and accuracy may vary depending on the con-sidered environment and the available GIS information. Forinstance, in the countryside, 𝑛 can be decreased still achievingan accuracy similar to the one achieved in urban environments.By contrast, in case no GIS information is available, 𝑛 shouldbe increased to achieve a reasonable accuracy of the bestcandidate selection.

III. ENHANCED TIME-FORWARDING TRACKING (ETFT)

Our tracking method is based on a time-forwarding al-gorithm. This algorithm uses the n candidates selected ateach point in time to set up a directed acyclic graph, calledextended Time-Forwarding Graph (eTFG), and to identify a setof paths compliant with the GIS map and motion constraints.A filtering stage, called Time-Forwarding Filtering (TFF), is


Fig. 3. ETFT candidates selection process.

applied to eTFG, using a likelihood-based approach, to selecta subset of paths (shadow paths) and to identify the best path.2

Then, the filter selects the best node as the candidate position.Figure 3 shows an architectural view of ETFT that is describedin the remainder of this section.

A. Extended Time-Forwarding Graph (eTFG)

A TFG is a direct acyclic graph [1], where every node 𝑝𝑖represents one of the possible positions of the mobile terminal,while edges, defined by the node pairs they connect (i.e., bysource and destination nodes), represent motion between them.Each edge is associated with a weight, computed based ondestination reachability and map constraints. eTFG extendsthe notion of edge and includes a holistic path evaluation toproduce a set of paths compliant with all map and motionconstraints. The weight function 𝑊 is defined over each edge𝑒 = (𝑝𝑖,𝑡, 𝑝𝑗,𝑡+𝑘), where 𝑖, 𝑗 ∈ [1, . . . , 𝑛], 𝑘 ∈ [1, . . . ,𝑚], and𝑡 is the actual time, as follows:

𝑊 (𝑒,𝑚𝑎𝑝) =

{𝛿(𝑒,𝑚𝑎𝑝) 𝑖𝑓 𝛿(𝑒,𝑚𝑎𝑝) ≤ 𝑇ℎ(Δ)+∞ 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(1)

Δ represents the time difference described by the edge 𝑒. Asa result, each edge 𝑒 is marked as reachable and associatedwith the corresponding weight, if and only if the function𝛿, which provides the real distance between two nodes 𝑝𝑖,𝑡and 𝑝𝑗,𝑡+𝑘 based on the map (i.e., taking into account thepresence of buildings, street curves, and so on), is less thanthe threshold 𝑇ℎ(Δ). 𝑇ℎ(Δ) defines the maximum acceptabledistance between each node (based on the mobile terminalvelocity) and is a function of the time variation Δ. In otherwords, by using Equation (1), weights are put in a linearrelation with distances between nodes, modeling reachabilitybetween nodes, and enforcing all known map and motionconstraints. Each eTFG path is then composed by a maximumset of𝑚 edges and𝑚+1 nodes covering time from 𝑡 to 𝑡+𝑚.

By using the GSM sampling interval (≈ 500 ms), our eTFGtolerates EMF fluctuation and is realistic enough for TFF filter-ing purpose. eTFG is constructed using a two-step algorithm,as follows. In the first step, a weight function with fixed Δ = 1is used, considering only edges between consecutive temporalnodes. This generates an eTFG containing the set of edgesfor which 𝛿(𝑒,𝑚𝑎𝑝) ≤ 𝑇ℎ(1), and the set of nodes with atleast one valid inbound or outbound edge. This initial eTFG

2Shadow paths are used for recovery in case of best path failure.

however must be revised in the second step, since even a setof candidates nodes at distance Δ = 1 can be completelyunreachable due to EMF fluctuations. In the second step, weconsider edges between non-consecutive temporal nodes. Let𝑆𝑡+𝑘 be the set of the n candidates positions at time 𝑡 + 𝑘.Recalling that, the distance from a node 𝑝𝑖,𝑡 to a node 𝑝𝑗,𝑡+𝑘

is the same as the weight 𝑊 among them, a distance 𝐷 canalso be defined between a node 𝑝𝑖,𝑡 and a set 𝑆𝑡+𝑘 of nodesat time 𝑡+ 𝑘, according to the map, as follows:

𝐷(𝑝𝑖,𝑡, 𝑆𝑡+𝑘,𝑚𝑎𝑝) = 𝑚𝑖𝑛𝑗∈[1⋅⋅⋅𝑛](𝛿((𝑝𝑖,𝑡, 𝑝𝑗,𝑡+𝑘),𝑚𝑎𝑝)

)(2)

For each node 𝑝𝑖,𝑡, if no reachable node is found at time𝑡 + 𝑘, that is, 𝐷(𝑝𝑖,𝑡, 𝑆𝑡+𝑘,𝑚𝑎𝑝) = +∞, nodes at time𝑡 + 𝑘 + 1 are evaluated. If 𝐷(𝑝𝑖,𝑡, 𝑆𝑡+𝑘+1,𝑚𝑎𝑝) ∕= +∞,an edge between non-consecutive temporal nodes is added toeTFG. In our previous TFG implementation [1], if 𝑘+1 wasgreater than 𝑚, the path was considered unreachable withoutconsidering possible fluctuation error at the outer nodes. Forthis reason, we extend the forwarding edges in order to alwayscomplete the temporal window 𝑚 reaching an outer node,still maintaining the maximum number of nodes in a singlepath equals to 𝑚+1. This avoids extreme cases in which EMFfluctuation at time 𝑚 makes all candidates unreachable andtherefore the whole eTFG path inapplicable.

Summarizing, our eTFG includes three types of edges:∙ edges 𝑒 between two consecutive nodes, if W∕= +∞;∙ forwarding edges 𝑒 between two non-consecutive tempo-

ral nodes 𝑝𝑖,𝑡 and 𝑝𝑗,𝑡+𝑘, with 0 < 𝑘 ≤ 𝑚:1) 𝑚𝑖𝑛ℎ=1⋅⋅⋅𝑘−1(𝐷(𝑝𝑖,𝑡, 𝑆𝑡+ℎ,𝑚𝑎𝑝)) = +∞ and2) 𝐷(𝑝𝑖,𝑡, 𝑆𝑡+𝑘,𝑚𝑎𝑝) ∕= +∞.

∙ extended forwarding edges 𝑒 between two non-consecutive temporal nodes 𝑝𝑖,𝑡 and 𝑝𝑗,𝑡+𝑘, with 𝑚 <𝑘 ≤ 𝑚+𝑚/2

The left box in Fig. 3 shows an example of eTFG whereedges with black lines represent motion between two consec-utive temporal nodes, and edges with dashed lines model mo-tion between two non-consecutive temporal nodes. The dottedrectangular region identifies the set 𝑅𝑡 of root nodes used attime 𝑡 for the best candidate identification. By combining alledges and nodes in eTFG, a set of candidate paths that satisfythe 𝛿(𝑒,𝑚𝑎𝑝) ≤ 𝑇ℎ(Δ) inequality is generated, and couldbe used to calculate a set of filtered candidate positions forthe mobile terminal. In an earlier version of this work, weused the minimum path on the graph satisfying the weightfunction 𝛿(𝑒,𝑚𝑎𝑝) ≤ 𝑇ℎ(Δ) to obtain a preliminary set offiltered positions for the mobile terminal; our experiments inreal environments however have shown that this choice isnot optimal in presence of fluctuation effects. In addition,considering the time window used by eTFG, we need tomanage two particular cases. The first case produces a pathwith a single direct forwarding (or extended forwarding) edgethat connects one of the eTFG nodes at time 0 and one at time≤ 𝑚 (or ≤ 𝑚+𝑚/2). The generated path, that only containstwo nodes, must be filtered out since it is due to fluctuationerrors with high probability. The second case produces twoequal estimations in subsequent time instants, as the inboundand the outbound of eTFG. Although this path could be


considered as an error due to fluctuations, and then eliminated,it cannot be ignored when pedestrian walk is considered. Todeal with these problems, we postpone the selection of thecandidates to a filtering stage called Time-Forwarding Filtering(TFF), that performs a holistic evaluation of the paths.

Finally, our solution mitigates the high dependency on 𝑚by selecting a set of eTFG paths instead of one candidatepath. Note that GIS information (e.g., pedestrian-only areas ormotorways) can be used to tune the parameter𝑚 of eTFG [1].Using a high value for 𝑚 (i.e., a long term prevision), weobtain a “strong trend” prevision that filters out any out-of-trend movement and produces high accurate results in hightrend-correlation areas like motorways or oneway streets. Bycontrast, using a small value for 𝑚, we obtain good resultsfor pedestrian-only areas, where the motion is usually chaoticwithout any prevalent movement trend.

B. Time-Forwarding Filtering (TFF)

eTFG identifies a set of paths compatible with motion andmap constraints which need to be filtered out by TFF to choosethe path that better approximates the user movement. Themain idea of the TFF algorithm is to use a short time window(usually between 2 and 6 seconds) bounded by 𝑚, where themotion model of the user can be considered uniform withconstant velocity. The time window is moved from one instantto the consecutive and therefore the constant velocity modelis smoothed along the time.

Our filtering strategy is “time-forwarding” because a po-sition 𝐶𝐸𝑇𝐹𝑇,𝑡 is calculated by considering the sets of allcandidates from 𝑆𝑡 to 𝑆𝑡+𝑚 and the set of all shadow paths(selected by means of a likelihood-based approach). More indetail, our filtering solution is based on residual evaluationwith Kalman filtering that is used to smooth out the multipleposition estimates to form a coherent path [26]. We first modelthe mobile station’s state 𝑠(𝑡) at time 𝑡 as follows:

𝑠(𝑡) = [𝑥(𝑡) 𝑦(𝑡) 𝑥′(𝑡) 𝑦′(𝑡)]𝑇 (3)

where 𝑥(𝑡) and 𝑦(𝑡) define the terminal’s position, and 𝑥′(𝑡)and 𝑦′(𝑡) the velocity in the directions of the 𝑥 and 𝑦 axes.Using the standard Kalman assumption [26] that the motionis governed partly by a deterministic equation and partly bya random variable, the mobility state model can be expressedcompatibly with the two fundamental Kalman’s equations:

𝑠(𝑡) = 𝐴𝑠(𝑡− 1) +𝐵𝑢(𝑡− 1) + 𝑤(𝑡− 1) [𝑝𝑟𝑜𝑐𝑒𝑠𝑠]𝑧(𝑡) = 𝐻𝑠(𝑡) + 𝑣(𝑡) [𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡]

(4)where 𝑠(𝑡) is the state vector and 𝑧(𝑡) is the measured vector

at step 𝑡. The square matrix 𝐴 in Equation (4) associates thestate at time 𝑡 − 1 to the state at time 𝑡 by calculating theamount of movement due to velocity 𝑥′(𝑡− 1) and 𝑦′(𝑡− 1)in 𝑠(𝑡− 1), and is defined as follows.

𝐴 =

⎡⎢⎢⎣

1 0 𝑇 00 1 0 𝑇0 0 1 00 0 0 1

⎤⎥⎥⎦

In the standard Kalman formulation, matrix 𝐵 expresses acontrol action acting on control input 𝑢. Matrix 𝐻 correlatesthe state with the measure, that is, a position in the samespace. The random variables 𝑤 and 𝑣 represent respectivelythe process and measurement noise. They are assumed to beindependent of each other, white, and with normal probabilitydistributions 𝑝(𝑤) ∼ 𝑁(0, 𝑄) and 𝑝(𝑣) ∼ 𝑁(0, 𝐺), where𝑄 is the process noise covariance matrix and 𝐺 is themeasurement noise covariance matrix.

In our case, i) the controlled process is the motion of themobile terminal, while the measurement represents the roughlocations extracted after eTFG, ii) the control matrix 𝐵 isnot used, and iii) the deterministic part of state equation isthe constant velocity motion sampled every 𝑇 seconds (seematrix A).

Note that, by balancing the process and measurementnoises, it is possible to consider the motion model morereliable than the measure or vice versa. Here, we trust themotion model more because it limits the effects of thephysical phenomena, which usually degrade the quality ofthe location process on the field. Therefore, our solution isaimed at discovering the coherence of the mobile terminalcandidate movements with the constant velocity model, ratherthan assessing the variation in the motion model due to themeasurement process. To evaluate if a certain path is coherentwith the model, we adopt the well-known log likelihood ofthe Kalman residual, that is, the difference between the actualmeasurement vector 𝑧(𝑡) and the predicted measurement vec-tor 𝐻𝑠(𝑡), where 𝑠(𝑡) is the a priori estimate. We considerthis residual as a random vector 𝑉 with distribution accordingto a multivariate normal distribution with population meanvector 𝜇 and population variance-covariance matrix Σ. Thejoint density function of the random vector 𝑉 is as follows:

𝜑(𝑉 ) =

(1

2𝜋𝜎2

)𝑝/2

∣Σ∣− 12 𝑒𝑥𝑝

{−1

2(𝑣 − 𝜇)′Σ−1(𝑣 − 𝜇)

}

(5)where ∣Σ∣ denotes the determinant of variance-covariance

matrix Σ. Since the residual vector 𝑉 is a 2-dimensionalvector, 𝑝 is equal to two, and we obtain a bivariate normaldistribution

𝑉 = 𝑧(𝑡)−𝐻𝑠(𝑡) (6)

Σ = 𝐻𝑃 (𝑡)𝐻𝑇 +𝐺 (7)

where 𝐺 is the measurement noise covariance and 𝑃 (𝑡)is the a priori estimated error covariance. The mean vectorof the residual is chosen as 𝜇 equal to zero, and thereforeEquation (5) reaches the maximum value when 𝑣 is equal to𝜇 or the residual is zero.

In general, TFF associates a likelihood value, influenced bythe size of 𝑚, to each probable path candidate. The bigger is𝑚, the more the constant velocity trend needs to be satisfiedfor a long period of time. For instance, near crossroads 𝑚needs to be kept low since the constant velocity assumptioncan be violated considering a long period of time, while onmotorways the parameter 𝑚 should be increased to bettersatisfy the assumption of constant velocity.

The right box in Fig. 3 shows the paths candidate reductionin TFF, where the set of all shadow paths (selected by means


(a)

(b)

Fig. 4. An example of ETFT tracking for two consecutive times (a) and (b).

of a likelihood-based approach) is presented with black lines,the best path at time 𝑡 is presented with a bold line, the bestcandidate 𝐶𝐸𝑇𝐹𝑇,𝑡 is presented as a circle labeled with “X”,and the set 𝑅𝑡+1 of root nodes at time 𝑡+ 1 is shown insidea dotted rectangular region. The outcome of the TFF filteringstage is a set 𝑅𝑡+1 of position candidates (with ∣𝑅𝑡+1∣ ≤𝑛) and the best position 𝐶𝐸𝑇𝐹𝑇,𝑡 for time 𝑡. Each node in𝑅𝑡+1 defines the root of a shadow path that has not beendiscarded (i.e., a shadow path whose likelihood value is greaterthan a threshold) and will be used at time 𝑡 + 1 to estimate𝐶𝐸𝑇𝐹𝑇,𝑡+1.

Figure 4(a) and 4(b) show the entire ETFT process fortwo consecutive time instants. As said in Section II, in realenvironments, lookup table candidates (i.e., all points in Fig. 4)are often very sparse in the surrounding area. The ETFTtracking approach addresses this sparseness by including timecorrelation. In the example, the real positions are indicated bytwo circles, while the squares surrounding the candidate pointsidentify the eTFG candidates that satisfy the TFF likelihood(the set 𝑅 of root nodes of the shadow paths), meaning thatthey approach the constant velocity model with a likelihoodgreater than a threshold. The candidates surrounded by a

square and labeled with a “+” indicate the best positions forthe two time instants.

Summarizing, the time-forwarding tracking technique takesfull advantage from all available GIS information, such asarea classification, so that the validation of candidate locationsdepends on map constraints. In some geographical areas, theadditional information provided by the map can be poor orabsent; when this happens, our technique is anyway able todynamically build an information database by estimating allrelevant knowledge including speed and acceleration.

IV. MAP-BASED FILTERING

ETFT produces a set of best candidates that satisfy aforwarding temporal relation, but no location correlation isapplied in the ETFT filtering stage. This lack of locationcorrelation may introduce an intrinsic measurement error inthe tracking algorithm [27]. We therefore extend our solutionby providing an adjustment of our model that further refinesthe set 𝐶𝐸𝑇𝐹𝑇 of best candidates. The refinement relies on anadditional Kalmal filter that uses map information about theroads [28]. This Kalman filter aims to guarantee a path conti-nuity relation, especially when different candidates belong todifferent ETFT paths, and smooths out the multiple positionestimates to form a coherent path that better approximates theactual mobility. Our map-based Kalman filter is an indepen-dent module aimed at estimating the kinematic parameters ofthe target and is different from existing solutions including ourprevious constrained Kalman Filter with HSMM [1]. Amonggeolocation and tracking solutions based on filtering, oneinteresting work [29] proposes two algorithms for real-timetracking, location, and dynamic motion of a mobile station in acellular network. This method is based on pre-filtering and twoKalman filters (one to estimate the discrete command processand the other to estimate the mobility state). The mobilitymodel is built on a dynamic linear system driven by a discretecommand process that was originally developed for trackingmaneuvering targets in tactical weapons system [30]. Thecommand process is modeled as a semi-Markovian processover a finite set of acceleration levels. Another promisingtechnique proposed by Yang and Wang [31] is a Monte Carloalgorithm for cell handoff decision based on mobility trackingin cellular networks. The authors estimate the location andspeed of a mobile terminal to predict the signal strength infuture time instants, and exploit this prediction to forecastthe handoff time. The work in [32] studies the performanceof target tracking in the presence of nonlinear road con-straints using a constrained extended Kalman filter (EKF).Another class of filtering exploits time-based biased KalmanInteracting Multiple Model (IMM) filter to model the errorintroduced by a NLOS scenario [33]–[35]. Finally, the workin [36] presents a solution based on RSSI and map informationthat exploits particle filtering and is tested with data comingfrom a real network. This solution is less accurate than oursolution and requires additional computational costs, since1000 particles are used for the localization process. Note that,the filtering techniques in this paper are different from theabove described techniques. Our techniques in fact do notfilter the location of the mobile terminal using RSSI, butthey take the terminal’s most probable positions, and try to


enforce the map and motion model constraints to extract thebest candidate.

Map information can be included in the models in twoways: i) by tuning the model of motion dynamics, and ii)by modifying the measurements according to road restrictions.The latter may already have been taken into account, since theset 𝐶𝐸𝑇𝐹𝑇 is already filtered by ETFT. Option i) (i.e., tuningthe model of motion dynamics) involves different methodsto incorporate the road information in the dynamic model ofKalman equations. In general, the performance of a Kalmanfilter is strictly related to the correctness of the motion dynam-ics model; biases and bad performance can often result whentargets deviate unexpectedly from the constant velocity modelin the transient periods. A conventional way of modelingmaneuvers in target dynamics is via additive Gaussian noise,but maneuver acceleration is naturally discontinuous and maynot be well representable as noise. In our module, we adopta standard approach for incorporating the ground information,based on a dynamic adaptation of the noise variance formaneuvers. This results in a flexible solution that deals wellwith transversal maneuvers, which take place when a mobiledevice reaches a curve or changes direction at road junctions.Other types of maneuvers (i.e., longitudinal maneuvers, suchas acceleration or jerking) frequent in vehicular motion cannotbe easily predicted since they depend on the individual driver’sbehaviour. However, their direction and magnitude can beroughly assessed based on the map. As the mobile terminalapproaches the end of a road segment, the probability ofmaneuver increases. Therefore the value of process variancecan be tuned, considering distance to the next waypoint andthe estimated velocity as well. In this context, we use a plainnoise model with a white acceleration model. The accelerationin 𝑥 and 𝑦 directions 𝑎 = [𝑎𝑥, 𝑎𝑦]

𝑇 is assumed to be constantin the time interval, not to exhibit autocorrelation, and to havemagnitude distributed as ∼ 𝑁(0, 𝜎2𝑎𝐼). The resulting map-based Kalman filter is similar to the one used in ETFT (seeEquation (4)) but the random variable 𝑘 is defined as follows:

𝑘 = 𝐺(𝑇 )𝑎

𝐺(𝑇 ) =

⎡⎢⎢⎣

12𝑇

2 00 1

2𝑇2

𝑇 00 𝑇

⎤⎥⎥⎦ (8)

where 𝑇 is the sampled interval. 𝑄, that is, the covariance ma-trix of the normal probability distributions of 𝑝(𝑤) ∼ 𝑁(0, 𝑄),becomes:

𝑄(𝑇 ) = 𝜎2𝑎

⎡⎢⎢⎣

14𝑇

4 0 12𝑇

3 00 1

4𝑇4 0 1

2𝑇3

12𝑇

3 0 𝑇 2 00 1

2𝑇3 0 𝑇 2

⎤⎥⎥⎦ (9)

This filtering avoids biases due to transient mismatchesbetween the constant velocity model and transversal accel-erations, but increases the noise in the estimators. To handlethis, we also exploit option ii) (i.e., modify the measurementsaccording to road restrictions) by using the measurementcovariance matrix 𝐺:

Fig. 5. Map-based Kalman (regular line) vs standard Kalman filtering (dottedline).

𝐺 =

[𝛼2𝑥 𝛼𝑥𝑦

𝛼𝑥𝑦 𝛼2𝑦

](10)

Considering the measurement covariance matrix 𝐺 and 𝜃,that is, the orientation of the road with respect to the horizontalaxis, as well as the set 𝐶𝐸𝑇𝐹𝑇 of measurements projected tothe road segments, the map-related covariance matrix 𝑅𝑝 isobtained from the original measurement covariance matrix 𝐺in the following way:

𝛼2𝑝 = 𝛼2

𝑥𝑐𝑜𝑠2(𝜃) + 𝛼2

𝑦𝑠𝑖𝑛2(𝜃)

𝛼2𝑥𝑝 = 𝛼2

𝑝𝑐𝑜𝑠2(𝜃) + 𝑤2𝑠𝑖𝑛2(𝜃)

𝛼2𝑦𝑝 = 𝛼2

𝑝𝑠𝑖𝑛2(𝜃) + 𝑤2𝑐𝑜𝑠2(𝜃)

𝛼𝑥𝑦𝑝 = (𝛼2𝑝 − 𝑤2)𝑐𝑜𝑠2(𝜃)𝑠𝑖𝑛2(𝜃)

(11)

𝑅𝑝 =

[𝛼2𝑥𝑝 𝛼𝑥𝑦𝑝

𝛼𝑥𝑦𝑝 𝛼2𝑦𝑝

](12)

where 𝑤2 is related to transversal variance inside the roadand depends on the road width. It is clear that inaccuracy inlocation produces incorrect transformation of measurements,with degradation even more severe than the ones due todynamic model correction.

One of the main advantages of Kalman filtering is thatit allows to model measurement and system errors indepen-dently. In our setting, measurement errors model the accuracyof the location candidates, while system errors represent theaccuracy of trajectory prediction. Figure 5 shows a comparisonbetween our map-based Kalman and the standard one. Aposteriori error covariances of standard Kalman are presentedwith dotted circles, while the ones of map-based Kalmanwith solid ellipses. Two consecutive real positions of themobile device are labeled with “+” symbol, the measuredpositions are presented with triangles, the locations usingmap-based Kalman with squares, and the locations predictedwith traditional Kalman with small circles. It is clear that, byadapting a posteriori error covariance of the Kalman filteringto the roads information, we obtain a great improvement interms of quality of geolocation as showed by our experimentalresults.

V. EXPERIMENTAL RESULTS

We extensively validate our map-based geolocation algo-rithm in a field environment, dealing with physical phenom-ena. We set up an experimental environment organized as


TABLE ICOMPARISON OF ERROR IN EMF PREDICTION. FOR EACH TRIP, DURATION

IN SECONDS (DUR.), NUMBER OF INVOLVED ANTENNAS (ANT.), ANDERROR-VARIANCE OF THE ERROR (IN DB) FOR EACH COMBINATION OF

INFORMATION ARE PRESENTED.

Exp. Dur. Ant. LOS NLOS NLOS+E NLOS+S NLOS+S+E

E1 3000s 54 49.3 - 7.0 17.4 - 6.3 12.5 - 7.9 13.0 - 6.0 11.6 - 6.6

E2 4000s 39 48.3 - 6.9 18.5 - 7.1 14.9 - 7.2 12.8 - 7.2 9.4 - 8.1

E3 1089s 22 42.9 - 5.4 22.4 - 6.3 20.0 - 7.0 14.6 - 7.9 11.3 - 6.3

E4 1443s 37 48.8 - 7.1 20.5 - 8.2 16.3 - 7.8 13.1 - 7.8 9.9 - 8.6

E5 959s 20 41.9 - 9.9 21.1 - 9.2 18.8 - 8.2 14.9 - 5.5 13.0 - 4.7

E6 789s 30 51.4 - 8.5 18.7 - 9.2 16.5 - 11.6 13.4 - 8.5 10.1 - 7.9

E7 133s 16 52.4 - 15.0 17.5 - 6.6 17.4 - 6.1 14.1 - 7.6 10.4 - 6.2

E8 1119s 37 47.7 - 6.9 19.2 - 7.7 17.8 - 8.8 12.3 - 5.6 10.7 - 5.9

E9 2099s 52 49.7 - 6.6 17.2 - 5.7 15.4 - 6.0 12.8 - 5.9 10.5 - 5.8

E10 899s 39 48.4 - 6.5 18.8 - 6.3 17.0 - 7.5 12.1 - 5.1 12.2 - 5.4

E11 929s 22 43.1 - 5.4 22.2 - 6.4 13.4 - 7.8 15.1 - 7.4 13.1 - 7.7

E12 1761s 26 46.1 - 6.4 24.2 - 8.5 15.6 - 8.5 14.5 - 7.2 12.7 - 6.2

Mean - - 47.5-7.6 19.8-7.3 16.3 - 7.9 13.6-6.8 11.2-6.6

follows (more details are available at http://sesar.dti.unimi.it/ctd.html). First, we performed twelve trips by car in the city ofMilan, the second largest metropolitan area in Italy providingan urban environment that includes parks and skyscrapers,over a month of experimentation. Trip duration varied from2 minutes to 1 hour. Information related to serving andneighboring cells coupled with GPS latitude and longitude wascollected, using cellular phones, every 0.48 seconds. Then, weimplemented a simulator of our geolocation algorithm and weperformed different types of testing over real network datafor evaluating EMF prediction quality (see Section V-A) andfor assessing geolocation quality with respect to the actualposition of a moving cellular phone (see Section V-B). Ourexperiments show the importance of environmental data forEMF prediction, that is in turn fundamental to establish theamount of filtering needed. In the following, we compare oursolution with traditional approaches (e.g., COST231, DCM).Comparison with other solutions is difficult to achieve sincethey usually consider artificial data. Moreover, the comparisonof solutions considering different urban environments canresult in misleading guessing due to the physical phenomenadiscussed in this paper.

A. EMF Prediction Evaluation

The quality of EMF prediction represents a fundamentalaspect towards the definition and development of an accuratelocation prediction and tracking algorithm. Traditional solu-tions are not well suited for complex urban environmentswhere physical phenomena, such as reflection, can involvebuildings and obstacles of different shapes and materials.Since the information required for high quality EMF predic-tion could not be included in standard GIS map, we proposean analysis of the quality of EMF prediction in relation withthe availability of such information.

We computed EMF values using a grid size of 10 metersand considering different combinations of information takenfrom our GIS map. Our grid size allows for fast computationand is consistent with the suggested grid resolution for RFEMF [37]. Selected combinations of map information are:i) omnidirectional antennas with no environmental information(LOS), ii) omnidirectional antennas with environmental infor-

mation (building structure with no elevation) (NLOS),3 iii) en-vironmental information and building elevation (NLOS+E),iv) environmental information and antenna’s shape (NLOS+S),and v) all information, including building elevation and an-tenna’s shape (NLOS+S+E). To compute the quality of theEMF prediction using the above combinations, we calculatethe error for each point in the considered trips as the differencebetween the predicted EMF and the one received by thecellular phone. As we will observe in the following, the erroris highly dependent on the amount of available informationused in the EMF prediction (i.e., shape of antennas, buildingelevation). An overview of the results for the considered paths(E1-E12) is presented in Table I. The mean error (see last rowof Table I), expressed in decibel (Db), is reduced from 47.5Db for LOS to 19.8 Db for NLOS, with a gain of 58.3%.In a field environment, however, this improvement is notenough. As shown in the “Mean” row of Table I, taking intoaccount building elevation (NLOS+E) boosts the precision ofthe EMF prediction by reducing the mean error of 3.5 Db withrespect to NLOS (i.e., a gain of 17.7%). Taking into accountdirectional antennas (NLOS+S), instead, boosts the precisionof the EMF prediction by reducing the mean error of 6.2 Dbwith respect to NLOS (i.e., a gain of 31.3%). We note that theEMF prediction quality for NLOS+E and NLOS+S is highlydependent on the type of path. For instance, in path E1 withprevalent frontal exposition to cell tower orientation (the setof serving cells plus the six best cells), NLOS+E outperformsNLOS+S. By contrast, in path E7, NLOS+S outperformsNLOS+E. In general, by considering both building elevationand antenna shapes (NLOS+S+E), our modified COST231outperforms traditional LOS and NLOS COST231 by reducingthe mean error of 76.4% and of 43.4%, respectively. Generallyspeaking, it is clear that the more abundant the contextualinformation is, the more the quality of the EMF prediction willbe. Contextual information, in fact, allows to partially take inaccount the physical phenomena influencing EMF prediction.However, there is a subtlety to consider when partial orerroneous map information is available. In such a case, theEMF calculation can result in unpredictable behaviours thatneed to be managed carefully. Experiment E10 in Table Iconsiders a path for which partial map information is available,that is, we do not consider building elevation in the first partof the path. This case results in an EMF prediction with poorprecision, which has performances similar to the NLOS+Scase (i.e., the one with no building elevation). Also, it showsthe importance of GIS map information and confirms the factthat outliers resulting from imprecision in building elevationcan substantially degrade the EMF calculation process.

As clear by the results of our analysis, the mean error inEMF prediction decreases significantly by adding the shapeof antennas and real building elevation map of the area(NLOS+S+E) to the standard COST 231 approach (NLOS).Obviously, since our experiments consider different trips inthe city of Milan, the improvement may also depend on thecoverage of each trip area. For instance, in case of trips withprevalent frontal exposition to cell tower orientation (e.g., E1in Table I), the improvement is less than in other cases.

3Points i) and ii) represent the traditional COST231 algorithm use cases.


TABLE IICOMPARISON OF LOCATION RESULTS (ERROR-MEAN SQUARE ROOT IN

METERS) USING: i) TRADITIONAL DCM, ii) ETFT, AND iii)ETFT+MAP-BASED KALMAN APPROACHES.

Traditional DCM DCM with Multiple CandidatesApproach Approach

Exp. NLoS+S+E ETFT ETFT+Map-BasedKalman

E1 212.3 - 43.2 39.1 - 20.4 35.3 - 22.5E2 204.3 - 43.6 38.4 - 22.5 34.6 - 23.5E3 215.2 - 41.9 39.6 - 21.1 36.1 - 21.9E4 200.1 - 41.6 34.1 - 20.4 31.2 - 21.4E5 218.9 - 44.2 41.1 - 21.5 36.7 - 22.3E6 198.1 - 42.9 33.0 - 21.9 30.7 - 22.6E7 201.5 - 44.8 34.5 - 22.3 31.7 - 22.6E8 205.2 - 41.7 37.5 - 20.7 34.6 - 21.4E9 201.2 - 42.3 35.1 - 20.2 33.4 - 20.9E10 204.3 - 44.2 39.8 - 22.3 36.5 - 23.1E11 230.1 - 43.6 41.1 - 21.8 38.0 - 22.9E12 207.2 - 42.6 35.2 - 20.3 31.4 - 21.1Mean 208.2 - 43.1 37.4 - 21.3 34.2 - 22.2

B. Location Prediction Evaluation

We take the accuracy of the geolocation using DCM withLeast Mean Square (LMS) described in [25] as a reference toevaluate our algorithm. We used the same twelve trips and en-vironmental setting to test both the traditional DCM approachand the solution in this paper. In the field, geolocation basedon LMS achieves a mean error, measured in distance from thereal GPS position, of 208.2𝑚 with less than 2% of estimationsunder the 50 meters of error when NLOS+S+E is used.The situation is even worse when the amount of contextualinformation decreases. Mean errors of 591𝑚, 302𝑚, 287𝑚,and 260𝑚 are achieved when LOS, NLOS, NLOS+E, andNLOS+S information is available, respectively. These errorsare mainly due the fact that EMF prediction cannot deal withthe fluctuation of the real EMF. Although it is possible toreduce the effects of this fluctuation by means of filtering,the obtained results are not good enough, since the localizednature of this fluctuation makes the filter difficult to tune.

Our strategy, that uses a set of multiple candidates for eachpoint in time, drastically reduces the localization error. We set𝑛=20 possible candidates, which gives a good balance betweenperformance and quality of the location process, and reducesthe mean error [1]. However, the best position estimation canbe still far from the real one, since the fluctuation effects aretoo high. To overcome these problems, an ETFT-based strategyand a map-based Kalman filtering have been introduced,smoothing the general location quality (see Table II).

The eTFG state reduction preserves a significant locationquality, since the majority of the best candidates, over the setof n probable ones, is preserved after the eTFG filtering. Thehighest rate of best candidates selected is achieved for pathE5 (with value of 94.2%), while E4 has the worst result (withvalue of 90.1%). In general, all the paths achieve a rate greaterthan 90% with a mean value of 91.9%. It is important tonote that, in the residual 8.1% of missing best candidates, the65% represents those cases that are far from the real locationdue to a considerable error in EMF prediction. To select thebest candidate, eTFG candidates are given in input to the TFFfiltering, which identifies the best 𝐶𝐸𝑇𝐹𝑇,𝑡 at time 𝑡, and asubset 𝑅𝑡+1 of root candidates to prevent drift effects during

tracking and calculate the best candidate at time 𝑡+ 1.There is still a possibility of jumping from one path to

other (including all shadow paths) during 𝐶𝐸𝑇𝐹𝑇 generation,losing the time correlation. To prevent this unwanted effect, amap-based Kalman filter is applied to re-enforce the time cor-relation and produce a fully traceable path. The results of ourexperiments are summarized in Table II, where NLOS+E+Ssingle candidate approach is compared with ETFT and ETFTwith map-based Kalman filtering approaches. The resultsshow that ETFT approach achieves a mean error of 37.4𝑚against the 208.2𝑚 of NLOS+S+E, with a gain of 82%.The application of the map-based Kalman filtering gives anadditional error reduction (mean error of 34.2𝑚) with a gainof 8.6% over the ETFT approach, and a negligible increaseof the mean standard deviation (less than one meter) thatdoes not affect our results. In this respect, it is importantto note that, due to fluctuation effects, ETFT-based solutionmay not be able to generate some of the locations along thepath. This problem is solved by map-based Kalman filteringthat provides an estimation also for those positions that havenot been calculated with ETFT. Table II shows a worst case(unlikely) scenario in which estimations are not used, andtherefore only locations calculated by ETFT are considered.In this scenario, we only perform selection among locationsgenerated by ETFT.

We also compare our solutions (i.e., ETFT only and ETFTwith map-based filtering) with the results achieved when thebest candidate in the set of 𝑛 candidates is manually selectedby looking at the real GPS-based mobile terminal position.The candidate that gives the minimum error with respect to thereal user position is selected and a path connecting the selectedcandidates in the time window under consideration is gener-ated. We observe that both ETFT only (37.4𝑚) and ETFTwith map-based filtering (34.2𝑚) approaches outperform theresults given by the best candidate selection (38.7𝑚). Theseresults show, on one side, that the best candidate selectionis affected by environmental conditions and errors in EMFprediction, and, on the other side, the suitability of our filteringstrategies which make our solution stronger against physicalphenomena, such as fluctuation. More in detail, the selectionof the best candidate considers a set of candidates that hasbeen populated based on the DCM lookup table in Section II.The accuracy of this selection is improved by our algorithmby considering i) the mobility of the user (Section III), thatis used to remove from a path those points that, although arethe best candidates for a given time instant, are not accuratedue to EMF fluctuation, and ii) map information about theroads (Section IV) to smooth out the overall path. To conclude,based on the results presented in Table II, Fig. 6 shows theCumulative Distribution Function (CDF) of the location errorfor path E2 (selected randomly) by plotting on the 𝑦 axisthe probability of a location error less than the correspondingvalue on the 𝑥 axis. Figure 6 shows that 5% of the predictedpositions has less than 4.0465 meters of error; 50% less than18.8491 meters; and 95% less than 63.7955 meters.

VI. CONCLUSION

Mobile terminal geolocation is an essential pre-requisitefor a wide range of applications on GSM/3G networks. For


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80

Cumulative Distribution Function

Error (m)

(4.0465, 0.05)

(18.8491, 0.5)

(63.7955, 0.95)

Fig. 6. Cumulative distribution function of the location error for path E2.

instance, geolocation can enable social networks which willlet users know when a friend happens to be nearby, or context-aware search engines which can help finding a recommendedrestaurant in an unfamiliar corner of town. The trend towardminiaturization and lower cost of GPS chipsets suggested thatsatellite-based geolocation systems like GPS could be the“silver bullet” of mobile phone geolocation. However, fewlocation-based services based on GPS besides road navigationare currently available on cell phones.

In this paper, we employed a novel time-forwarding trackingalgorithm with GIS map constraints and a map-based Kalmanfiltering for error correction purposes, and we showed that theGSM/3G-based geolocation techniques can successfully chal-lenge satellite-based techniques, natively supporting a numberof LBSs, such as identification and certification of mobileterminals trajectories when terminals are on board vehicles.Our “network-centric” (as opposed to GPS “device centric”)terrestrial geolocation technique promises to be more robustthan other popular geolocation systems, such as, WPS, w.r.t.location spoofing and similar security attacks. In addition, oursolution can be integrated with existing sources of locationto compensate their location error and improve the overalllocation performances in those environments that are usuallyimpervious to them. As an example, our algorithm can be use-ful when GPS-based approaches fail and lost connectivity, e.g.,in urban or indoor environments, thus providing continuousestimation of the mobile device position. We also presenteda complete experimentation in the field, confirming that high-precision geolocation can be obtained by using statistical EMFprediction even in presence of fluctuations.

The computational approach presented in this paper in-creases real-time awareness of terminal position on the partof the GSM/3G networks, paving the way to future work inlocation-dependent modulation [38], adaptive cell sizing, andnetwork management platforms based on mobility predictiontechniques. Also, the information on user spatial distributioncan be used for network dynamic planning in hybrid urbanand indoor scenarios, e.g., supporting location-aware handoffbetween 3G and WiFi network protocols.

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Marco Anisetti received the Ph.D. degree in com-puter science from the Universita degli Studi di Mi-lano in 2009. He is currently a research collaboratorat the Dipartimento di Tecnologie dell’Informazione,Universita degli Studi di Milano, Italy. His mainresearch interests are in computer vision, imageprocessing, with special regard to tracking strategiesand emotional state estimation by facial analysis. Heis also involved in several research project regardingGSM protocol and mobile phone electromagneticfields prediction.

Claudio A. Ardagna is an assistant professor atthe Dipartimento di Tecnologie dell’Informazione,Universita degli Studi di Milano, Italy. He receivedthe Ph.D. degree in computer science from the Uni-versita degli Studi di Milano in 2008. His researchinterests are in the area of information security,privacy, access control, mobile networks, and opensource. He is the recipient of the ERCIM STM WG2009 Award for the Best Ph.D. Thesis on Securityand Trust Management. The URL for his web pageis http://www.dti.unimi.it/ardagna

Valerio Bellandi received the Ph.D. degree in com-puter science from the Universita degli Studi di Mi-lano in 2009. He is currently a Post-Doctoral Fellowat the Dipartimento di Tecnologie dell’Informazione,Universita degli Studi di Milano, Italy. His researchinterests are in computer vision, location algorithmand network communication protocol, with specialregard to feature extraction methods and emotionalstate estimation by facial analysis. He is also in-volved in several research projects regarding linkmanagement protocol in optical network.

Ernesto Damiani is currently a Professor at theUniversita degli Studi di Milano and the directorof the Universita degli Studi di Milano’s Ph.D.program in computer science. His areas of interestinclude mobile networks, business process represen-tation, Web services security, processing of semiand unstructured information (e.g., XML), modelsand platforms supporting open source development,and semantics-aware content engineering for mul-timedia. He is an Associate Editor of the IEEETRANSACTIONS ON SERVICE ORIENTED COMPUT-

ING, Area Editor of the Journal of System Architecture and a member ofvarious editorial boards. He has published several books and about 200 papersand international patents. Prof. Damiani is a Senior Member of the IEEE andACM Distinguished Scientist.

Salvatore Reale headed the Radio Access NetworkManagement R&D at Siemens TLC in Italy. He is afounder and member of Board of Directors of ITA-STQB, the Italian Board of ISTQB (InternationalSoftware Testing Qualifications Board) for the in-ternational qualification scheme of Certified Testers.He is Founder and Owner of Partner EngineeringOffice at GREENGEGNERIA.IT. His interests arein ICT and energy areas.

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