=Paper=
{{Paper
|id=Vol-3434/139
|storemode=property
|title=GNSS Jammer Localization in Urban Areas Based on Carrier-to-Noise Ratio and Classification Methods
|pdfUrl=https://ceur-ws.org/Vol-3434/paper9.pdf
|volume=Vol-3434
|authors=Zhe Yan,Ahmed Al-Tahmeesschi,Titti Malmivirta,Laura Ruotsalainen
|dblpUrl=https://dblp.org/rec/conf/wiphal/YanAMR23
}}
==GNSS Jammer Localization in Urban Areas Based on Carrier-to-Noise Ratio and Classification Methods==
https://ceur-ws.org/Vol-3434/paper9.pdf
GNSS Jammer Localization in Urban Areas Based on
Carrier-to-Noise Ratio and Classification Methods
Zhe Yana , Ahmed Al-Tahmeesschia , Titti Malmivirtaa and Laura Ruotsalainena
a
Department of Computer Science, University of Helsinki, Helsinki, Finland
Abstract
Global Navigation Satellite Systems (GNSS) are the primary sources of accurate Position, Navigation,
and Time (PNT) information to critical infrastructures. As a result, the localization of an intentional
jamming source is an important step in securing GNSS resilience as it provides the authorities with
technical tools to prevent the jamming action. However, conventional jammer localization methods
are all in a way limited in urban areas, and the non-line-of-sight and multipath receptions that
are frequently encountered are not well addressed. So in this work, a ray-tracing method is used
to simulate the jamming propagation in a real urban environment, and a receiver characterization
method is provided to obtain the effective carrier-to-noise ratio measurement. Besides, different
support-vector-machine-based methods are used to determine the jammer location as a classification
problem. A preliminary result with a validation accuracy of 96.2% is provided and proves the
feasibility of this method. In the end, the drawbacks and future work plan are summarized.
Keywords
GNSS, jammer localization, urban areas, classification, machine learning
1. Introduction
Critical infrastructures are assets that are considered so vital to the security of supply that
their failure will seriously affect national security, economic security, or public health and
safety. Such infrastructures and services rely heavily on accurate Position, Navigation, and
Time (PNT) information [1]. For example, banking transactions, stock markets, and electricity
transmission systems use accurate timing for synchronization, while rescue services, aviation,
and logistics use accurate and reliable positioning for vehicle operations. Global Navigation
Satellite Systems (GNSS) are the primary sources of PNT information to such critical services
due to their wide availability and the low cost of user devices. So GNSS itself is commonly
considered a part of critical infrastructures, and it is strongly regulated and monitored by
authorities.
The GNSS signals are vulnerable and can be easily affected by natural or man-made interfer-
ence [2, 3]. Among them, the intentional interference called jamming, namely the transmission
of signals at the GNSS frequency bands masking the underlying real signals, degrades the accu-
racy and availability of GNSS PNT services and is therefore illegal in most countries. Necessary
countermeasures include jamming detection and mitigation, and jammer localization [4].
WIPHAL 2023: Work-in-Progress in Hardware and Software for Location Computation, June 06–08, 2023,
Castellon, Spain
" zhe.yan@helsinki.fi (Z. Yan); ahmed.al-tahmeesschi@helsinki.fi (A. Al-Tahmeesschi);
titti.malmivirta@helsinki.fi (T. Malmivirta); laura.ruotsalainen@helsinki.fi (L. Ruotsalainen)
0000-0001-8055-1555 (Z. Yan); 0000-0002-5750-5080 (A. Al-Tahmeesschi); 0000-0002-4057-4143 (L.
Ruotsalainen)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
Localization of an intentional jamming source is an important step in securing GNSS re-
silience as it provides the authorities with tools to prevent the continuation of a detected
jamming action. However, the complex signal propagation in dense urban areas makes local-
ization a challenging problem for conventional techniques. Generally, the measurements for
jammer localization can be categorized as Received Signal Strength (RSS)/Differential Re-
ceived Signal Strength (DRSS), Angle of Arrival (AOA)/Direction of Arrival (DOA), Time
of Arrival (TOA)/Time Difference of Arrival (TDOA), and Frequency Difference of Arrival
(FDOA) [5, 6]. The possibility of using automatic gain control (AGC) for jammer localization
has been studied in [7], where the 2D-position of an interference source can be solved by the
AGC values from at least three monitoring stations. However, the AGC may saturate [7] and
the localization accuracy declines rapidly for long-distance interference source [8]. Use of DRSS
can solve the problem of unknown transmitter power [5], [9] and [10]. However, for dense urban
environments, the none-line-of-sight (NLOS) reception and multipath [11] effect may deterio-
rate the positioning accuracy significantly, because even simple ground reflections can severely
affect the RSS-based methods [12]. AOA is not as popular as other methods because it has the
highest implementation complexity. To obtain the AOA measurements, antenna arrays or the
antenna that can be precisely rotated or moved are needed [4, 13]. AOA performance is also
poor in the presence of NLOS and multipath signals [5]. TDOA has been widely used and well
developed in radio-network-based navigation systems, but it requires accurate synchronization
of the receiver clock among all the monitors to acquire range information, which brings dif-
ficulties to implementation. The main disadvantage of TDOA is that it is only suitable for
wide-band interference localization [14]. One way to improve this is to use TDOA in combina-
tion with some other localization techniques, for example, [15] combines it with AGC, and [14]
with AOA, while [16] suggests the jointly estimation of TDOA and FDOA. Another problem
is that GNSS signals themselves, as the in-band signals for the jamming, will cause additional
peaks in the cross-correlation for TDOA systems [17]. in order to localize moving emitters.
Mainly working well for narrow-band interference, FDOA requires the relative movement be-
tween the jammer and monitor nodes as well as precise timing and frequency synchronization,
and is usually used together with TDOA [18, 19].
Carrier-to-noise ratio (C/N0 ) is commonly used as an indicator for jamming detection [20],
and is receiving increasing attention to be used as the measurement for jammer localization.
Because (C/N0 ) requires no complex equipment, and methods using it can be easily imple-
mented using off-the-shelf receivers. By constructing a model that describes the variation of
C/N0 as a function of the distance between the target receiver and interference source, the
localization performance is validated without considering the NLOS and multipath reception
[4, 21]. LOS, NLOS and diffraction loss models are used to simulate the jamming signals, and
a linear formula is fitted to obtain the corresponding effective C/N0 in [8], but the simulation
details are not given.
As a result, though using C/N0 as the measurement for jammer localization is a promising
and attractive method, the limitation in urban area remains a significant problem. The ef-
fective C/N0 impacted by reflected and diffracted jamming signals is difficult to be modeled.
Fortunately, the well-explored ray-tracing technologies in mobile-communication community
and the use of machine learning models provide good tools to solve this problem. So in this
work, ray-tracing technology is used to simulate the jamming propagation in a real-life urban
area. And the effective C/N0 outputs of a commercial GNSS receiver under jamming are
modeled. Then, the obtained effective C/N0 is used as the measurement, and the jammer
localization is described as a classification problem.
The rest of this article is organized as follows: the effective carrier-to-noise ratio model is
given in section 2. Then, the ray-tracing propagation model is introduced in section 3. Section
4 describes the receiver characterization and modeling. Next, in section 5, a preliminary result
is provided which demonstrates the effectiveness of this method in urban jammer localization.
At last, the work plan for the future is given.
2. Effective Carrier-to-noise ratio
Supposing C/N0 is the carrier-to-noise ratio without jamming, the effective C/N0 output of
the receiver under jamming can be modeled by
Ci Ci 1
Ci /N0 |ef f = = · , (1)
N0 + kJ N0 1 + k NJ
0
where Ci is the received signal power of the ith satellite, N0 is the noise power spectral density,
and J is the received jamming power. k is the Spectral Separation Coefficient (SSC) which
models the filtering effect of the receiver on the jamming signal. (1) is converted to log scale
as
(︃ )︃
J
Ci /N0 |ef f,dB - Hz = Ci /N0 |dB - Hz − 10log10 1 + k . (2)
N0
After this conversion to logarithmic scale, we can obtain the commonly used C/N0 that is
expressed in dB-Hz. Assuming that the jamming power J is considered significantly larger
than noise N0 , namely k NJ0 ≫ 1, we can obtain [4]
(︃ )︃
k
Ci /N0 |ef f,dB - Hz ≈ Ci /N0 |dB - Hz − 10log10 − J|dBW , (3)
N0
where the jamming power J is expressed in dBW.
Using the jamming resistance quality factor Q, another expression of (1) given by [8] is
1
C/N0 |ef f = 1 J
, (4)
C/N0 + SQRC
where S is the satellite signal power and RC is the spreading code rate. (4) is converted to log
scale as (︃ )︃
C/N0
C/N0 |ef f,dB - Hz = C/N0 |dB - Hz − 10log10 1 + J . (5)
SQRC
Assuming that the jamming signal power is much greater than the satellite signal, we can
obtain (︃ )︃
C/N0
C/N0 |ef f,dB - Hz ≈ C/N0 |dB - Hz − 10log10 − J|dBW , (6)
SQRC
where SQRC actually functions in the same way with k.
Figure 1: Lay out of the buildings in the simulated area (Sello shopping center area, Espoo, Finland).
3. Ray-tracing propagation model
We have experimented the method using simulated signals. A ray-tracing technique based on
real-life city model is developed to guarantee the high fidelity of signal propagation in urban en-
vironments. Different from the theoretical and empirical models which provide simple formulas
for the path-loss calculation, such as, [4], [8], and the standards recommended by the Interna-
tional Telecommunication Union-Radiocommunication Sector (ITU-R), ray-tracing is a general
propagation modeling tool that provides estimates of path loss, angle of arrival/departure, and
time delays by numerically solving Maxwell’s equations [22].
Herein, the Shooting and Bouncing Ray (SBR) [23] provided by MATLAB and the city
model from OpenStreetMap are used to simulate the jamming signal propagation loss in Sello
shopping center area of Espoo, Finland. The layout of this area can be seen in Figure 1.
By the ray-tracing method introduced in this section, the received jamming power J at each
monitor can be obtained.
4. Receiver characterization and modeling
Apart from the J obtained from section 3, the spectral separation coefficient k or the jamming
resistance quality factor Q needs to be modeled to obtain the effective C/N0 measurement,
according to (3) or (6). Though these two values can be learned automatically in the training,
we need to determine k first to simulate a jamming scenario. To keep the high fidelity with
the future practical real-life validation, a popular and low-cost Ublox F9P GNSS receiver is
chosen for the effective C/N0 modeling. Generally, most of the jamming devices transmit a
swept tone waveform (chirp) which can be generated from inexpensive devices and is quite
effective in rendering GNSS inoperable [2, 10]. Here, the chirp signal transmit powers ranging
from -130 ˜-35 dBm by step 5 dBm, in a 10 MHz band centered at L1 1575.42 MHz, and with
a sweep time of 10 µs were simulated. The chirp signal can be modeled as the combination of
Figure 2: GUI of Orolia GSG-8 GNSS constellation simualtor and the simulation in Sello shopping center
area, Espoo, Finland.
multiple saw-tooth functions according to the following expressions[10]
(︄ +∞ (︃∫︂ )︃)︄
∑︂ t (︁ ′
∫︂ t
′
)︁ (︁ ′ )︁ ′
x (t) = a sin 2π f1 t − h · Tsw,1 · dt + · · · + fn t − h · Tsw,n · dt , (7)
h=0 0 0
{︃
f0,n + ku,n t, 0 ≤ t < Tu,n
fn (t) = (8)
f0,n + (ku,n − kd,n ) Tu,n + kd,n t, Tu,n ≤ t < Tsw,n
where fn (t) represents the nth saw-tooth function with the starting frequency of f0,n . ku,n and
kd,n are the positive and negative slope of the saw-tooth function respectively, and Tu,n and
Td,n the increasing and decreasing time duration of the saw-tooth function respectively. Tsw,n
is the sweep time.
The GPS L1 C/A signal jammed by the chirp signals from an Orolia GSG-8 constellation
simulator, shown in Figure 2, was input into the F9P receiver, and the C/N0 outputs under
different jamming power were logged. The effective C/N0 of GPS SVN 05, SVN 23, and SVN
29 are drawn in Figure 3.
A linear formula was used in [8] to fitting the linear part below -95 dBm . In this work,
to simulate the multipath impacted jamming scenario, the whole range in Figure 3 was fitted
using Fourier series. To keep the balance between the accuracy and avoiding over-fitting, 3-4
components are recommended. It is also important to make the lower-frequency component
the main part. The fitting results are shown in Figure 4.
The effective C/N0 model provided here is established according to Ublox F9P, to obtain
the C/N0 output of the F9P given a jamming power value from the ray-tracing introduced
previously in section 3.
45
SVN 05
40 SVN 23
SVN 29
Effective carrier-to-noise ratio/dB-Hz
35
30
25
20
15
10
5
0
-130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30
Interference Power/dBm
Figure 3: Effective carrier-to-noise ratio (GPS L1 C/A) of Ublox F9P with respect to different interference
power
5. Simulation and localization experiments
5.1. Simulation settings
In our ray-tracing simulation, an urban area about 0.5 km2 around the Sello shopping center
of Espoo, Finland was chosen. 7 monitoring nodes were placed 2 m above the building roofs,
and 60 jamming emitters were randomly generated on each street out of the 4 streets around
the block. So totally 240 jammer samples and 240×7×3 C/N0 measurements from 7 nodes
and 3 satellites were obtained. One of the ray-tracing examples can be seen in Figure 5. The
maximum reflections for each path were set to 5, and the reflections with a relative path loss
greater than 40 dB were discarded. The materials of the building and terrain were both set as
concrete.
It needs to be noted that the multipath effect in a GNSS receiver is totally different from
the multipath effect that the jammer encounters. For the fixed GNSS monitors on building
roofs in this simulation, multipath changed slowly (for hours) and could be modeled as static.
So it was unnecessary to keep GNSS multipath environment exactly the same with the one
of jammer multipath. In a more complex scenario, multipath modelling details need to be
considered.
5.2. Localization using classification methods
By modelling the variation of C/N0 as a function of the distance between the target receiver and
jamming source, the jammer may be localized by optimizing a cost function which combines
all the C/N0 s of the monitoring nodes [4, 21]. However, the NLOS and multipath signals make
the relation between C/N0 and the distance ambiguous and thereby difficult to be modeled.
A possible solution is to do the modelling using machine learning, namely as a multi-class
SVN 05 SVN 23
45 45
C/N0 from F9P C/N0 from F9P
40 Fourier fitting 40 Fourier fitting
Effective carrier-to-noise ratio/dB-Hz
Effective carrier-to-noise ratio/dB-Hz
35 35
30 30
25 25
20 20
15 15
10 10
5 5
-130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30 -130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30
Interference Power/dBm Interference Power/dBm
(a) (b)
SVN 29
45
C/N0 from F9P
40 Fourier fitting
Effective carrier-to-noise ratio/dB-Hz
35
30
25
20
15
10
5
0
-130 -120 -110 -100 -90 -80 -70 -60 -50 -40 -30
Interference Power/dBm
(c)
Figure 4: Model fitting of the effective carrier-to-noise ratio (GPS L1 C/A) of Ublox F9P: (a) SVN 05; (b)
SVN 23; (c) SVN 29.
classification task.
Support vector machines (SVMs) are supervised learning models used for classification and
regression problems. SVMs have already been used for jammer localization in [8]. Herein,
SVMs using different kernel functions are tested to determine which street the jammer is on.
The models include linear, quadratic, and cubic SVMs, and fine, medium, and coarse Gaussian
SVMs. The last three methods make finely detailed, medium, and coarse distinctions between
classes with kernel scale set to sqrt(P)/4, sqrt(P), and sqrt(P)*4 respectively, where P is the
number of predictors. In the validation, a 5-fold cross-validation method is used, and the
results can be seen in Figure 6 and Table 1.
According to our experimental results, Cubic SVM achieves the best performance with 96.2%
validation accuracy, while Quadratic SVM performs similarly with an accuracy of 95.8%. This
demonstrates the potential of using the C/N0 measurements and classification to localize jam-
mers in an urban area with NLOS and multipath propagation. According to the results pre-
sented with confusion matrices, the classification mistakes mainly appear between the adjacent
streets. Except for fine Gaussian SVM, rare confusions appear between the street 1 and 3, and
the street 1 and 4, as seen in Figure 5. This can be partly attributed to the 4 monitors on the
Figure 5: Description of the ray-tracing paths between the jammer and monitors in Sello shopping center
area, Espoo, Finland. The jammer was located on the 4 streets (street 1: up; 2: left; 3: down; 4: right)
around the block.
Table 1
Localization accuracy of the different methods
Fine Medium Coarse
Linear Quadratic Cubic
Method Gaussian Gaussian Gaussian
SVM SVM SVM
SVM SVM SVM
Accuracy 92.5% 95.8% 96.2% 80.4% 94.2% 92.5%
street corners because they are usually totally blocked and have no jamming reception. This
leads to a conclusion that the placement of the monitors, to a certain extent, is important for
jammer localization.
6. Conclusion and future work
Conventional jammer localization methods are all in a way limited in urban areas where the
NLOS and multipath receptions are frequent, and the models between the measurements and
the ranges between the jammer and monitors become ambiguous. Machine learning-based
methods are powerful in solving the ambiguity problem and have not been well explored yet.
In this work, SVMs and the easily available C/N0 measurements were used for jammer lo-
calization. A preliminary result was provided, and the potential of this method was verified.
Also, a detailed jamming multipath simulation method based on receiver characterization was
provided and will serve as basis for more realistic further research.
Linear SVM Quadratic SVM
1 98.3% 1.7% 98.3% 1.7% 1 100.0% 100.0%
True Class
True Class
2 1.7% 91.7% 6.7% 91.7% 8.3% 2 1.7% 93.3% 5.0% 93.3% 6.7%
3 18.3% 81.7% 81.7% 18.3% 3 10.0% 90.0% 90.0% 10.0%
4 1.7% 98.3% 98.3% 1.7% 4 100.0% 100.0%
TPR FNR TPR FNR
1 2 3 4 1 2 3 4
Predicted Class Predicted Class
(a) (b)
Cubic SVM Fine Gaussian SVM
1 100.0% 100.0% 1 70.0% 30.0% 70.0% 30.0%
True Class
True Class
2 1.7% 90.0% 8.3% 90.0% 10.0% 2 1.7% 70.0% 28.3% 70.0% 30.0%
3 5.0% 95.0% 95.0% 5.0% 3 3.3% 96.7% 96.7% 3.3%
4 100.0% 100.0% 4 15.0% 85.0% 85.0% 15.0%
TPR FNR TPR FNR
1 2 3 4 1 2 3 4
Predicted Class Predicted Class
(c) (d)
Medium Gaussian SVM Coarse Gaussian SVM
1 100.0% 100.0% 1 96.7% 3.3% 96.7% 3.3%
True Class
True Class
2 1.7% 91.7% 6.7% 91.7% 8.3% 2 5.0% 90.0% 5.0% 90.0% 10.0%
3 15.0% 85.0% 85.0% 15.0% 3 15.0% 85.0% 85.0% 15.0%
4 100.0% 100.0% 4 1.7% 98.3% 98.3% 1.7%
TPR FNR TPR FNR
1 2 3 4 1 2 3 4
Predicted Class Predicted Class
(e) (f)
Figure 6: Confusion matrices, true positive rates (TPR), and false negative rates (FNR) of different SVMs:
(a)Linear SVM; (b) Quadratic SVM; (c) Cubic SVM; (d) Fine Gaussian SVM; (e) Medium Gaussian SVM;
(f) Coarse Gaussian SVM.
This paper described the very first phase of our ongoing research. In our future research, we
will divide the simulated area in finer blocks to get more precise location solution. Secondly,
since the C/N0 is not reliable when the jamming is extremely strong, more measurements
should be utilized, such as AGC and AOA. Thirdly, we have previously developed an LSTM-
based anomaly detection method and based on the good results we will also use LSTM models
for jammer localization. At last, real-life data from our ARFIDAAS2 project will be used for
experiment validation.
Acknowledgments
This work was funded by the Academy of Finland project 338043 Resilience and security
of geospatial data for critical infrastructures (REASON), and the Department of Computer
Science, University of Helsinki.
References
[1] V. Vitan, G. Berz, L. Saini, J.-P. Arethens, B. Belabbas, P. Hotmar, Research on alterna-
tive positioning navigation and timing in Europe, in: 2018 Integrated Communications,
Navigation, Surveillance Conference (ICNS), Herndon, USA, 2018, pp. 4D2–1–4D2–17.
[2] T. Humphreys, Interference, in: P. J. Teunissen, O. Montenbruck (Eds.), Springer Hand-
book of Global Navigation Satellite Systems, 1st ed., Springer Nature, Gewerbestrasse,
Switzerland, 2017, pp. 469–503.
[3] Z. Yan, X. Chen, X. Tang, L. Ruotsalainen, A novel carrier loop based on adaptive LM-
QN method in GNSS receivers, IEEE Transactions on Vehicular Technology 71 (2022)
5259–5271.
[4] D. Borio, C. Gioia, A. Štern, F. Dimc, G. Baldini, Jammer localization: From crowdsourc-
ing to synthetic detection, in: Proceedings of the 29th International Technical Meeting
of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland,
Oregon, 2016, pp. 3107–3116.
[5] A. G. Dempster, E. Cetin, Interference localization for satellite navigation systems, Pro-
ceedings of the IEEE 104 (2016) 1318–1326.
[6] F. Šture, T. Morong, P. Kovář, P. Puričer, High performance sdr for monitoring system
for gnss jamming localization, in: 2019 International Conference on Wireless Networks
and Mobile Communications (WINCOM), Fez, Morocco, 2019, pp. 1–5.
[7] J. Lindstrom, D. M. Akos, O. Isoz, M. Junered, GNSS interference detection and lo-
calization using a network of low cost front-end modules, in: Proceedings of the 20th
International Technical Meeting of the Satellite Division of The Institute of Navigation
(ION GNSS 2007), Fort Worth, USA, 2007, pp. 1165–1172.
[8] D. Lyu, X. Chen, F. Wen, L. Pei, D. He, Urban area GNSS in-car-jammer localization
based on pattern recognition, Navigation 66 (2019) 325–340.
[9] S. Bartl, P. Berglez, B. Hofmann-Wellenhof, GNSS interference detection, classification
and localization using software-defined radio, in: 2017 European Navigation Conference
(ENC), Lausanne, Switzerland, 2017, pp. 159–169.
[10] M. Bartolucci, R. Casile, G. E. Corazza, A. Durante, G. Gabelli, A. Guidotti, Coopera-
tive/distributed localization and characterization of gnss jamming interference, in: 2013
International Conference on Localization and GNSS (ICL-GNSS), Turin, Italy, 2013, pp.
1–6.
[11] Z. Yan, L. Ruotsalainen, X. Chen, X. Tang, An INS-assisted vector tracking receiver with
multipath error estimation for dense urban canyons, GPS Solutions 27 (2023) 1–16.
[12] R. J. R. Thompson, E. Cetin, A. G. Dempster, Unknown source localization using rss in
open areas in the presence of ground reflections, in: Proceedings of the 2012 IEEE/ION
Position, Location and Navigation Symposium, Myrtle Beach, USA, 2012, pp. 1018–1027.
[13] E. M. Lloyd, R. J. Watson, Using a bio-inspired algorithm for efficient angle-of-arrival
estimation of GNSS jammers, in: 2019 13th European Conference on Antennas and
Propagation (EuCAP), Krakow, Poland, 2019, pp. 1–5.
[14] E. Cetin, R. J. R. Thompson, A. G. Dempster, Passive interference localization within
the GNSS environmental monitoring system (GEMS): TDOA aspects, GPS Solutions 18
(2014) 483–495.
[15] J.-H. Lee, H.-P. Kim, J.-H. Won, GNSS cloud-data processing technique for jamming
detection and localization, in: ITSNT 2018, International Technical Symposium on Nav-
igation and Timing, Toulouse, France, 2018. URL: https://hal-enac.archives-ouvertes.fr/
hal-01942259.
[16] J. A. Bhatti, T. E. Humphreys, B. M. Ledvina, Development and demonstration of a
TDOA-based GNSS interference signal localization system, in: Proceedings of the 2012
IEEE/ION Position, Location and Navigation Symposium, Myrtle Beach, USA, 2012, pp.
455–469.
[17] E. Cetin, M. Trinkle, A. Bours, G. Gabelli, R. J. Thompson, A. G. Dempster, G. E.
Corazza, Overview of weak interference detection and localization techniques for the GNSS
environmental monitoring system (GEMS), in: Proceedings of the 27th International
Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+
2014), Tampa, USA, 2014, pp. 2250–2259.
[18] G. Pojani, Y. Abdoush, M. Bartolucci, J. A. Garcia-Molina, G. E. Corazza, Multiple
jammer localization and characterization based on time-frequency analysis, in: 2018 9th
ESA Workshop on Satellite NavigationTechnologies and European Workshop on GNSS
Signals and Signal Processing (NAVITEC), Noordwijk, Netherlands, 2018, pp. 1–8.
[19] J. Garcia-Molina, M. Crisci, Snapshot localisation of multiple jammers based on receivers
of opportunity, in: 2016 8th ESA Workshop on Satellite Navigation Technologies and
European Workshop on GNSS Signals and Signal Processing (NAVITEC), Noordwijk,
Netherlands, 2016, pp. 1–6.
[20] D. Borio, C. O’Driscoll, J. Fortuny, GNSS jammers: Effects and countermeasures, in:
2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) European
Workshop on GNSS Signals and Signal Processing, Noordwijk, Netherlands, 2012, pp.
1–7.
[21] N. Ahmed, N. Sokolova, RFI localization in a collaborative navigation environment, in:
2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland,
2020, pp. 1–13.
[22] Z. Yun, M. F. Iskander, Ray tracing for radio propagation modeling: Principles and
applications, IEEE Access 3 (2015) 1089–1100.
[23] H. Ling, R.-C. Chou, S.-W. Lee, Shooting and bouncing rays: calculating the rcs of an
arbitrarily shaped cavity, IEEE Transactions on Antennas and Propagation 37 (1989)
194–205.