Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Abstract

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

Full text

sensors
Article
Jammer Classification in GNSS Bands Via Machine
Learning Algorithms
Ruben Morales Ferre 1, Alberto de la Fuente 2and Elena Simona Lohan 1,*
1ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland;
ruben.moralesferre@tuni.fi
2GMV Aerospace & Defence, GNSS Department, 28760 Madrid, Spain; afuente@gmv.com
*Correspondence: elena-simona.lohan@tuni.fi
Received: 9 September 2019; Accepted: 1 November 2019; Published: 6 November 2019


Abstract:
This paper proposes to treat the jammer classification problem in the Global Navigation
Satellite System bands as a black-and-white image classification problem, based on a time-frequency
analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning
approaches in order to sort the received signal into six classes, namely five classes when the jammer
is present with different jammer types and one class where the jammer is absent. The algorithms
based on support vector machines show up to 94.90% accuracy in classification, and the algorithms
based on convolutional neural networks show up to 91.36% accuracy in classification. The training
and test databases generated for these tests are also provided in open access.
Keywords:
jamming; Global Navigation Satellite Systems (GNSS); classification; image processing;
deep learning; Convolutional Neural Networks (CNN); Support Vector Machines (SVN)
1. Introduction and Motivation
Jamming threats are increasing in the Global Navigation Satellite System (
GNSS
) bands.
According to recent Eurocontrol Voluntary ATMincident reports [
1
], the number of Global Positioning
System (
GPS
) outages has been increasing exponentially over the past five years, and these numbers
are predicted to increase further. The main cause of
GPS
outages is the presence of jamming signals in
the
GPS
frequency bands, i.e., L bands around 1.5 GHz carrier frequency. A jamming signal can be
typically divided into human-made or channel based. Human-made interferences can be split into
intentional (e.g., jamming or spoofing) or unintentional (e.g., interference produced by other systems
such as inter-modulation products or radio resource allocation). Channel based interferences involve
phenomena such as multi-paths, atmospheric scintillation, or fading. Jamming then can be defined
as an intentional (usually narrowband) interference in the wireless bands of interest, with received
powers of several orders of magnitude higher than the useful received powers, in this case the powers
of the
GNSS
signals. The high received power differences between the jammers and the
GNSS
signals
are due to the fact that the jammers are typically placed on Earth or in the vicinity of the Earth’s surface
(e.g., drone installed jammers), while the
GNSS
satellites are about 19–23 thousands km above the
Earth’s surface; thus, the path-loss attenuation is significantly higher for the
GNSS
signals than for the
jammer signals.
Given the increased prevalence of interferences in GNSS bands, the focus in the GNSS literature
in recent years has shifted towards integrity methods and procedures to deal with jamming and
other interference signals, such as meaconing and spoofing. There are several jamming management
solutions, such as detection, mitigation, localization, or classification. The focus in the literature
so far has encompassed all-but-jamming classification solutions. For example, jamming detection
solutions were reported in [
2
–
4
]; jamming mitigation algorithms can be found in [
2
,
5
–
7
]; and jamming
Sensors 2019,19, 4841; doi:10.3390/s19224841 www.mdpi.com/journal/sensors

Sensors 2019,19, 4841 2 of 15
localization was addressed in [
3
,
8
]. However, very little effort has been dedicated so far to the
jamming classification algorithms, which are also an important stage towards an efficient interference
management solution. According to [
9
], there are several main classes of jammers. The classification
we adopt here encompasses five main jammer classes, namely: (i) Amplitude Modulated (
AM
)
jammers, (ii) chirp jammers, (iii) Frequency Modulated (
FM
) jammers, (iv) pulse jammers or Distance
Measurement Equipment (
DME
)-like jammers, and (v) Narrow Band (
NB
) jammers. There is also
a sixth category of Wide Band (
WB
) jammers, but this is typically very hard to detect or classify as
the signal characteristics in the time and frequency domain are very similar in the presence and in
the absence of jammers. This similarities are due to the fact that the
GNSS
signal is also a wideband
signal, having a noise-like power spectral density. In our paper, we will focus on the classification
of the five categories mentioned above, (i) to (v), in addition to being able to classify correctly the
absence of jammers (i.e., no jammer present). To the best of the authors’ knowledge, currently, there
is no published work on the classification results of jamming types in
GNSS
bands. Related works
deal with
NB
jammer classification [
10
,
11
] and chirp signal classification [
12
], but only in the context
of radar systems.
The paper’s contributions are as follows: (i) proposing to treat the jammer classification as
a black-and-white image classification problem, based on the spectrograms computed at the GNSS
receiver from the Intermediate Frequency (
IF
) samples; (ii) proposing efficient Support Vector Machines
(
SVM
) and Convolutional Neural Network (
CNN
) based classifiers with classification accuracy of 90%
or more; (iii) offering to the research community an open-access comprehensive database of clean and
jammed
GNSS
signal spectrograms for a variety of Carrier-to-Noise Ratio (
C/N0
) and Jammer-to-Signal
Ratio (JSR) conditions.
The rest of the paper is organized as follows: Section 2gives a mathematical modeling of the
considered jammer types; Section 3formulates the jammer classification into an image classification
problem by using a short-time short-frequency decomposition of the incoming signal; Section 4gives
an overview of the machine learning algorithms we propose to use for the classification purposes;
Section 5presents the results based on SVM and CNN; Section 6gives the references to the repository
where we made our data public; and Section 7summarizes the findings.
2. Jamming Types
If we consider r(t)as the signal reaching a GNSS receiver, r(t)can be modeled generically as:
r(t) = g(t) + j(t) + s(t) + w(t), (1)
where
g(t)
represents the signals of interest, coming from the
GNSS
constellation of satellites,
j(t)
is
a possible jamming signal including adjacent-band interference (e.g., harmonics from other systems
close to
GNSS
bands), and
s(t)
denotes a possible spoofing signal. The background noise over the
wireless propagation channel is modeled as
AWGN
and denoted by
w(t)
. In the absence of jamming,
j(t) =
0, and in the absence of spoofing,
s(t) =
0. In our studies, we will concentrate on the case of no
spoofers, but jamming is present, i.e., j(t)6=0 and s(t) = 0.
As can be found in the literature [
9
,
13
], the jamming signals
j(t)
can be typically divided into
several jamming classes, typically according to the difficulty in detecting them. Below, we present the
mathematical models of the jammers considered in our study.
2.1. AM Jammers
This class of jammers contains the simplest and at the same time the most studied jammer types,
and they are also referred to as Continuous Wave (
CW
) jammers. We would like to emphasize that the
AM noise jammers are different from the AM/CW simple jammers described here; our
AM
jammers
refer to single- or multi-tone sinusoidal signals, weighted with various amplitudes, as shown below
in Equation
(2)
.
AM
jammers can be single-tone or multi-tone. An
AM
-modulated jammer can be

Sensors 2019,19, 4841 3 of 15
modeled using Equation
(2)
and consists of
k=
1,
. . .
,
K
single tones (
K=
1 for the single-tone case,
and
K>
1 for the multi-tone case). It is characterized mainly by three parameters, namely
PJk
,
fJk
, and
θJk
, which stand for the power at the antenna and the corresponding carrier frequency and phase of
the kth jammer component, respectively.
j(t) =
K
∑
k=1qPJkexp(j(2πfJkt+θJk)) (2)
2.2. Chirp Jammers
This category contains jammer signals whose frequency is modulated linearly over time. They are
generated by sweeping linearly their frequency over a certain frequency range and a certain time
period, after which the process is started again at the initial frequency. These saw-tooth chirp jammers
can be modeled by Equation
(3)
. The parameters shown in Equation
(3)
are: the jamming power
PJ
,
the starting frequency of the sweep
fJ
(at time
Tswp =
0), the minimum and maximum frequency
sweep
fmin
and
fmax
, and the sweep period
Tswp
, which is the time it takes the jammer to sweep
from
fmin
to
fmax
. The variable
b=±
1 is a flag determining if we have an up-chirp (
b=
1) or
a down-chirp (
b=−
1);
θJ
denotes the initial phase of the jammer; and
fq(t) =
2
πfJt+πb(fmax−fmin )
Tswp t2
is the instantaneous frequency of the jamming signal.
j(t) = pPJ·exp(j(2πfJt+πb(fmax−fmin )
Tswp t2+θJ))
=pPJ·exp(j(fq(t)t+θJ)) (3)
2.3. FMJammers
FM
jammers can also be single-tone or multi-tone. An
FM
jammer can be modeled using
Equation
(4)
, for which the carrier frequency is time dependent. Equation
(4)
incorporates a parameter
βk
into the signal model, which is the modulation index of the
kth
tone. For the single-tone case,
K=
1.
j(t) =
K
∑
k=1qPJkexp(j(2πfJkt+βk·sin(2πfJkt))) (4)
2.4. Pulse Jammers or DME-Like Jammers
These are jamming signals that are active only during repetitive periods of time with an active
period of a pulse called the “duty cycle”. Pulse jammers can be modeled using Equation
(5)
, where
pτ(t)is a rectangular pulse of width τ(the duty cycle), fris the pulse repetition frequency, δ(t)is the
Dirac pulse, and ⊗is the convolution operator.
j(t) = qPJpτ(t)⊗
K
∑
k=1
δt−k
frk·exp(j2πfJkt)(5)
2.5. NB Jammers
The narrowband noise jammers are jammers that span a narrow band of the signal spectrum,
e.g., by sending a higher power signal in that spectral region. Such jammers can be modeled using
Equation
(6)
, where
β
is the modulation index and
n(ζ)
represents a stationary random process with
zero mean and σ2
ζvariance.
j(t) = qPJcos 2πfJt+βZt
0n(ζ)dζ+θJ(6)

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3. Novel Problem Formulation of Jammer Classification
The different jammer signals described in Section 2affect in different manners the received signal
r(t)
. The time-frequency information of
r(t)
carries significant information about the possible presence
of jammers. As some of the jammer types are non-stationary, as is the case of the chirp jammers, the best
time-frequency modeling is a short-time short-frequency transform such as the signal spectrogram.
In order to compute the spectrogram, first we generate a short signal of 1 ms in duration, containing
jamming and the useful signal or the useful signal alone. The short-term short-frequency spectrogram
transform relies on parameters chosen as a trade-off between achieving a good resolution for all the
considered jammer types and maintaining a low complexity. Therefore, the spectrogram has a window
length of 128 samples with an overlap of 120 samples and an Fast Fourier Transform (
FFT
) size of 128
samples. Figure 1gives examples of different spectrograms that can be obtained for the considered
jamming signal types. The full-colored spectrograms are depicted here, while their black-and-white
exemplification will be shown in Section 5.
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(a)
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(b)
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(c)
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(d)
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(e)
50 100 150 200 250 300 350
Time ( s)
-20
-10
0
10
20
Frequency (MHz)
(f)
Figure 1.
Spectrograms of common jamming signals in the baseband. All graphs contain a signal
mixture of the jamming signal and the
GPS
L1 signal, where the
C/N0
is 45 dB and the
JSR
is 50 dB.
(a) No jammer. (b)AM jammer. (c) Chirp jammer. (d)FM jammer. (e) Pulse jammer. (f)NB jammer.
To obtain the plots in Figure 1, the
C/N0
and
JSR
were set to 45 dBHzand 50 dB, respectively, in
order to have a clear view of the jammer presence. For all the simulations carried out, the chosen

Sensors 2019,19, 4841 5 of 15
GNSS constellation and frequency band was
GPS
L1, which corresponds to 1.57542 GHz. In Figure 1a,
the spectrogram of the clean
GNSS
is given (i.e., no jammer present), while the sub-plots, Figure 1b–f,
show examples of the spectrograms for the five considered jammer signals. In the absence of jamming,
we observed a
WB
signal composed basically of noise, since there was no other contribution than the
GNSS
signal itself, which is a low-power Code Division Multiple Access (
CDMA
) signal and thus
noise-like. On the contrary, for the rest of the scenarios in Figure 1b–f, a certain contribution from
a certain jammer type can be noticed. For example, in Figure 1b or Figure 1d, we notice horizontal
straight lines at a certain frequencies, which correspond to the frequencies of the
AM
or
FM
tones’
contribution, respectively, of the jamming signal
j(t)
. In Figure 1c, we observe the typical spectrogram
of a saw-tooth signal showing the sweep range and sweep periods of an up-chirp signal, as a typical
saw-tooth signal. Figure 1e shows a double-pulse signal active before 50
µ
s. Finally, in Figure 1f, the
spectrogram shows that there exists a certain contribution of band-limited noise, which only affects a
certain (narrow) bandwidth of the signal r(t).
In this article, we propose a methodology in order to identify and classify the different jammer
types based on the analysis of such a spectrogram image. The methodology is depicted in the
block diagram of Figure 2. The raw data corresponding to
r(t)
and containing both the
GNSS
and
interference signals (if any) were used first to produce a spectrogram, as the ones shown in Figure 1.
The spectrogram image was then saved as a black-and-white image (monochrome .bmp image) in
order to reduce the number of (irrelevant) features. It was saved with a resolution of 515
×
512 pixels
and 600 DPI. After that, one of the chosen machine learning algorithms was applied. The choice
of a black-and-white image instead of the colored one was motivated by our empirical studies, as
well as by the intuition that
SVM
classifiers are known to work better with binary images than with
multi-level images.
The choice of the machine learning algorithms for classification was also based on literature
studies. We selected
SVM
and
CNN
, as described in Section 4, based on their reportedly good
performance in image classification problems, as described in Section 4. These algorithms classify
the images according to the different features they can extract from them into six different classes,
namely one of the five jammer class types described above or no-jammer class.
Raw Data Spectrogram Image
Suport Vector
Machine
Convolutional
Neural Network
Classified
Jammer Type
Figure 2. Block diagram of the proposed methodology.
4. Machine Learning Algorithms
In this section, the implemented machine learning methods to perform the classification are briefly
described. For the
SVM
method, proposed for example in Vapnik [
14
,
15
], we basically extracted the
image features from the black-and-white images by using the method called Bag of Features (
BoF
) [
16
].
These features were used as input data to the
SVM
classifier. The
SVM
classifier included two
stages: a training stage based on training data available and a classification stage based on new data
(called test data). The second studied classifier was the
CNN
classifier, based on multi-layer neural
networks [
17
]. The reason to choose
SVM
and
CNN
as classifiers was their excellent performance for
various classification problems reported in the recent literature [18–20].

Sensors 2019,19, 4841 6 of 15
4.1. Image Features: Bag of Features (BoF)
A bag of features method consists of representing images based on extracting local features.
The name comes from the Bag of Words (
BoW
) representation used in textual information retrieval [
16
].
Analogous to
BoW
, by which we can represent a certain document as a normalized histogram of word
counts and build-up a word dictionary, by using
BoF
, we can do a similar thing, but using image
features instead of words. The representative features of the images were gathered into clusters, which
contained similar features. Every time a new representative feature was detected, it was matched to
the nearest cluster feature from the visual vocabulary. At a high level, the procedure for generating
a bag of features image representation can be summarized into three steps [16]:
1.
Vocabulary building: First of all, the features from all the training images were extracted.
These features were stored in a “visual vocabulary” dictionary, where each feature represented
a “visual word” or “term”.
2.
Term assignment: After extracting the features, they were gathered into clusters. The clusters
collected the closest terms in the vocabulary dictionary in order to reduce the complexity.
3.
Term-vector generation: The term vector was generated by recording the counts of each term that
appeared in the image to create a normalized histogram counting the times it was repeated in the
cluster. This term vector was the bag of features representation of the image.
The set of features contained in the term vector were used by
SVM
to classify the data into the
different classes.
4.2. Support Vector Machines (SVM)
SVM
consists of a set of supervised learning methods that can be used for classification, regression,
and/or ranking. Supervised learning means that the user must take part during the training process
of
SVM
. The user must label the training data previous step of training
SVM
.
SVM
uses machine
learning theory as a principle of operation to learn and to perform classification.
SVM
performs
classification by transforming (if needed) the original training data into multidimensional space
and constructing a hyper-plane with it. The established hyper-planes will split the different classes
to classify. The hyper-plane with the maximum distance between support vectors, which are the
closest data points to the hyper-plane, is the hyper-plane used to classify, and it is called the optimal
hyper-plane. Therefore, our goal was to maximize the margin between the edge data points and
the hyper-plane in order to be able to classify with the minimum miss-classification error. Figure 3
shows a two-class separation scenario. The green squares represent the positive class, and the red dots
represent the negative class. The bold green squares and red dots are the support vectors, which were
the samples that were the most difficult to classify because they were at the border between both data
types and were used to determine which was the optimal hyper-plane. The optimal hyper-plane in
Figure 3is drawn with a dotted line, and it was the hyper-plane with the maximum distance between
the different support vectors. In this example, the classification can be easily performed since the
data were linearly separable, and we only had two classes. For more than two classes, a graphical
representation is harder to draw, but the reasoning and extension to
N
classes is straightforward
(N=6 in our case).
In order find the optimal hyper-plane, we could start defining a hyper-plane as:
wTx+b=0 (7)
where
wT
is the normal vector to the hyper-plane containing the weights toward the different data
points,
x
contains the points defining the hyper-plane, and
b
is a bias constant. Our dataset was
composed by
z=z1,z2, ..., zm
and
yi∈ −1, 1
being the class label of
z
, which was
−
1 or one for the
negative and positive class of the example, as shown in Figure 3, respectively. We can now define the
decision boundary, which should classify all points correctly as:

Sensors 2019,19, 4841 7 of 15
wTz+b≥1i f yi=1
wTz+b≤ −1i f yi=−1
Support Vectors
Positive Class
Negative Class
Maximum
Margin
Figure 3. SVM binary classification.
Among all the hyper-planes separating the data
z
, there exists an optimal hyper-plane yielding
the maximum margin of separation between the classes to classify. This optimal hyper-plane can be
found by optimizing Equation (7) as:
max
{w,b}min{k x−zk:x,z∈ <N,(wTx+b=0)}(8)
The weight vector
w
will consider the values of
z
closer to the hyper-plane with a higher value,
the closest ones of each class being the support vectors, as is shown in Figure 3.
To find the optimal hyper-plane, different methods can be used. Some of the most popular are the
Iterative Single Data Algorithm (ISDA) [
21
], L1 soft-margin minimization by Quadratic Programming
(L1QP) [22], and Sequential Minimal Optimization (SMO) [23].
Typically, most classification tasks cannot be separated as easily as in Figure 3, and often, more
complex or higher order structures are needed in order to make an optimal separation, such as is
shown in Figure 4. When we could not find a linear separator, the data points were typically projected
into a higher dimensional space where the data points became linearly separable. This transformation
was carried out with functions called kernels. Basically, a kernel maps the non-linear separable dataset
into a higher dimensional space where we can find a hyper-plane that can separate the samples linearly.
One example of this could be to map the 2D data representation into a 3D representation. It might
happen that in 3D space, the data can be easily separable by a simple straight line, as in Figure 3.
There are multiple kernel types that we could use to transform the data. Some of the most popular
ones are the linear kernel, the polynomial kernel, and the Radial Basis Function (RBF) kernel.

Sensors 2019,19, 4841 8 of 15
Support Vectors
Positive Class
Negative Class
Maximum
Margin
Figure 4. SVM binary classification with non-linear separation.
The linear kernel is defined as:
K(zi,zj) = zi·zj(9)
where ziand zjare two different support vectors and K(·)is the kernel function.
The polynomial kernel could be expressed as:
K(zi,zj) = (zi·zj+c)d(10)
Finally, the RBF kernel is defined as:
K(zi,zj) = ex p(−γkzi−zjk2)(11)
The kernel choice in our studies is discussed in Section 5.2.
4.3. Convolutional Neural Network (CNN)
A
CNN
or Convolutional Neural Network (
ConvNet
) consists of a sequence of layers, where every
layer transforms the data into a simplified version through a differentiable function. The three main
types of layers to build
ConvNet
’s architectures are the convolutional layer, pooling layer, and fully
connected Layer. Our layer architecture, as is shown in Figure 5, consisted of:
1.
Input layer: converts the input image into
mxnxr
data usable for the following layers, where
m
and
n
are the height and width of the image in pixels, respectively, and
r
is the depth (e.g., one
for gray-scale images and three for Red Green Blue (RGB) images).
2.
Convolutional layer: computes the output number of neurons that are connected to local regions
of the input image by computing a dot product between their weights and a small region they
are connected to in the input. The convolutional layer will have
k
filters (also called kernels) of
size
sxtxq
where
s
and
t
must be smaller than the dimension of the image and
q
can either be the
same as the number of channels
r
or smaller and may vary for each kernel. In our architecture,
we used a 2D convolution composed by k=16 filters and a size of 12 ×12 ×1.
3.
ReLU layer: also known as the Rectified Linear Unit layer (ReLU), applies an element-wise
activation function, such as
max(
0,
x)
, to transform the summed weighted input from the node

Sensors 2019,19, 4841 9 of 15
into the activation of the node or output for that input. In other words, it will output the input
directly if it is positive; otherwise, it will output zero.
4.
Pool layer: This layer will perform a downsampling operation along the spatial dimensions
(width and height). For example, in our architecture, the pool size was [2,2], which means that if
the layer returns the values
"1 2
3 4#
, the maximum value in the regions of height two and width
two will be selected, which is four.
5.
Fully connected layer: The objective of a fully connected layer is to take the results of the
convolution/pooling process and use them to classify the image. The fully connected layer goes
through its own back propagation process to determine the most accurate weights. Each neuron
receives weights that prioritize the most appropriate label.
6.
Softmax layer: This layer limits the output of the previous step to classification into the range
zero to one. This allows the output to be interpreted directly as a probability.
7.
Classification layer: computes the cross-entropy loss for multi-class classification problems with
mutually exclusive classes. In other words, it performs the classification based on the output of
the previous layer.
To perform the training, we need a solver. Three of the most common are:
1. Stochastic Gradient Descent with Momentum (SGDM) optimizer [24].
2. Root Mean Squared Propagation (RMSProp) optimizer [25].
3. Adaptive moment estimation (Adam) optimizer [26].
Input
Layer
Convolutional
Layer ReLU Layer Pooling
Layer
Fully Connected
Layer Softmax Layer Classification Layer
Figure 5. Layer architecture list.
5. Results
5.1. Image Database Creation
Both Machine Learning (
ML
) algorithms described in Section 4need a set of images in order to be
trained for the later classification. This set of images is called the “training dataset”. The larger the
training dataset is, the better the algorithms can be trained. A different set of images called the “testing
dataset” was used for testing the classifier and evaluating the classifier’s accuracy. A third type of
database, called the “validation dataset”, may be additionally used for validating during the training
process, specially using CNN.
In order to create the databases, a large set of independent simulations was carried out, as
described in Section 5.2. The entire dataset contained 61,800 binary images (i.e., images were only
composed of ones (black) and zeros (white) pixels).
The whole database was then split in three parts: 6000 images for training purposes
(i.e., 1000 images per jammer type), 1800 images for validation purposes (i.e., 300 images per jammer
type), and 54,000 images for testing (i.e., 9000 images per jammer type). Thus, the dataset was split
as 10
/
90%, which means 10% for training/validation and 90% for testing purposes. Figure 6f shows
some examples of binary images used in the
ML
algorithms. The spectrogram looks similar to the
ones shown in Figure 1, but in binary scale instead of
RGB
and with a pixel resolution of 512
×
512.
Several resolutions were also tested in our studies, and a 512
×
512 resolution proved to give the
best results.

Sensors 2019,19, 4841 10 of 15
(a)(b)
(c)(d)
(e)(f)
Figure 6.
Binary spectrogram images of common jamming signals in the baseband. (
a
) No jammer.
(b)AM jammer. (c) Chirp jammer. (d)FM jammer. (e) Pulse jammer. (f)NB jammer.
5.2. Simulation Parameters
The simulations steps were the following:
•
Generating the
GNSS
signal plus one of the jammer types at a time, using random signal
parameters following a certain uniform distribution, as is summarized in Table 1.
AM
and
FM
tones were uniformly distributed between 0.1 MHz and 10 MHz. Chirps
Tswp
and
Fsw p
were
uniformly distributed between 5–20
µ
s and 5–20 MHz, respectively. The bandwidth for
NB
jammers was set between 20 MHz and 2 GHz. Finally, for the pulse jammer, the duty cycle (
τ
)
and repetition frequency (Fr) was set between 1–19 µs and 0.1–1.9 THz.
•
Sending the
GNSS
signal (with or without jammer) over a wireless channel with Additive White
Gaussian Noise (
AWGN
). The
C/N0
and
JSR
of the
GNSS
and jammer signals were set as well
following a uniform distribution between 25 dBHz and 50 dBHz and 40 dB and 80 dB, respectively
(i.e., CN0∼ U(25, 50)dBHz and JSR ∼ U(40, 80)dB).
•
Computing the spectrogram of the received signal and saving it as a black-and-white image in the
training, validation, or testing dataset
•
Applying the
SVM
and
CNN
algorithms described above to classify the test images, based on the
information contained in the training and validation image databases.

Sensors 2019,19, 4841 11 of 15
•
Computing the confusion matrix, counted as the percentages of correctly classifying each jammer
type (its diagonal values) and the percentages of misclassifying a jammer type (the other values
in the matrix except the diagonal values)
Table 1. Jammer parameters’ summary.
Jammer Type Parameter
AM jammer fJ∼ U(0.1, 10)MHz
Chirp Tswp ∼ U(5, 20)µs
Fswp ∼ U (5, 20)MHz
FM jammer fJ∼ U(0.1, 10)MHz
NB jammer Bandwidth ∼ U(20, 2000 )MHz
Pulse jammer τ∼ U (1, 19 )µs
Fr∼ U (1×1011, 19 ×1011)THz
The training process with
SVM
was built on a
K
-means clustering with a 500 word visual
vocabulary and a total number of features of 4,561,920. Not all the features were used to decrease the
computational complexity, but only 70% of the strongest features were used. The 70% parameter was
again fine-tuned based on experiments.
In terms of kernel choice, we used the
RBF
kernel since it works well in practice and it is relatively easy
to tune. In order to find the optimal hyper-plane, we used the Sequential Minimal Optimization (
SMO
)
method [
27
], which is an iterative algorithm based on Lagrange multipliers for optimization. It was
responsible for solving the Quadratic Programming (QP) during the training of SVM’s.
The
CNN
was trained during 25 epochs with the Adam algorithm [
?
] due to its good performance
and because it has been specifically designed for training deep neural networks. This means that the
entire training dataset was passed both forward and backward through the neural network 25 times
using the Adam algorithm in order to find the optimal hyper-plane.
5.3. Confusion Matrix Results
Figures 7and 8show the confusion matrix for the SVM and CNN algorithms, respectively.
The meaning of the different colors of each label box is summarized in the right side color
bar. The confusion matrix shows how accurate a classifier is, in terms of how well it classifies and
misclassifies the test dataset in the different classes it has.
Figure 7shows that the
SVM
algorithm had a mean accuracy of 94.90%.
SVM
was able to
determine if the jammer was present or not with an accuracy of more than 98%. Only less than 2%
of cases were misclassified as the jamming-present scenario when in fact there was no interference.
The classification of pulse and chirp jammers offered close to 100% accuracy with the testing dataset
used. The accuracy of
AM
and
FM
single-tone jammers was around 90%. Finally, we observed that
NB
jammers were classified correctly almost 94% of the time. These results are really promising, since
for example, we can determine the presence of interference with an accuracy of more than 98% or we
can determine if it is a pulse jammer with an accuracy of almost 100%. Especially important to notice
is that these results were achieved with a training dataset of only 6000 images.
Similarly, Figure 8shows that the
CNN
algorithm had a mean accuracy of 91.36%, which was
just about 3% smaller than using
SVM
, but still very high. The reason for the decrease in accuracy in
CNN
compared to
SVM
could be the fact that there were more parameters to optimize in
CNN
than in
SVM
, and finding rigorous mathematical rules for their optimization is an open challenge. Thus, there
might still be the possibility to increase the accuracy of
CNN
further by an improved optimization of
its parameters.
We also noticed that, by using
CNN
, the accuracy determining the presence of a jammer (versus no
jammer case) was slightly higher than for SVM, although only by 0.4%.

Sensors 2019,19, 4841 12 of 15
The pulse jammers’ classification accuracy was also similar to that of
SVM
, namely close to 100%.
The chirp jammers’ accuracy was more than 91%, but also about 5% lower compared with
SVM
.
The classification of
AM
and
FM
jammers showed an accuracy of almost 91% and 89%, respectively
(slightly smaller than with
SVM
). Finally, the poorest classification accuracy was obtained for the
NB
jammers, where we obtained an accuracy of only 78% (i.e., smaller by about 5% than the SVM case).
SVM Average Accuracy: 94.90%
99.99%
8999
3.34%
301
0.00%
0
2.72%
245
0.00%
0
0.23%
21
0.01%
1
94.19%
8477
0.87%
78
0.28%
25
2.34%
211
0.44%
40
0.00%
0
0.00%
0
98.56%
8871
0.00%
0
0.00%
0
0.00%
0
0.01%
1
0.07%
6
0.00%
0
90.92%
8183
0.11%
11
9.73%
876
0.00%
0
2.39%
215
0.57%
51
0.01%
1
96.06%
8645
0.03%
3
0.00%
0
0.01%
1
0.00%
0
6.07%
546
1.49%
134
89.57%
8060
Pulsed NB NoJam Single AM Single Chirp Single FM
Target Class
Pulsed
NB
NoJam
Single AM
Single Chirp
Single FM
Output Class
0
10
20
30
40
50
60
70
80
90
100
No Jam
No Jam
Figure 7. SVM confusion matrix.
CNN Average Accuracy: 91.36%
99.64%
8967
8.34%
751
0.0%
0
1.02%
92
0.04%
4
0.56%
50
0.3%
27
78.41%
7057
1.02%
92
0.23%
21
8.59%
773
0.13%
12
0.0%
0
0.0%
0
98.95%
8905
0.0%
0
0.0%
0
0.0%
0
0.04%
4
0.18%
16
0.01%
1
90.92%
8182
0.04%
4
10.39%
935
0.01%
1
12.98%
1168
0.02%
2
0.01%
1
91.32%
8218
0.0%
0
0.01%
1
0.09%
8
0.0%
0
7.82%
704
0.01%
1
88.92%
8003
Pulsed NB NoJam Single AM Single Chirp Single FM
Target Class
Pulsed
NB
NoJam
Single AM
Single Chirp
Single FM
Output Class
0
10
20
30
40
50
60
70
80
90
100
No Jam
No Jam
Figure 8. CNN confusion matrix.
6. Open-Source Data
The dataset of images used to achieve these results, as well as the MATLAB codes to run the
simulations using both ML algorithms are available in open access at Zenodo [28].

Sensors 2019,19, 4841 13 of 15
7. Conclusions
This paper presented a methodology to classify jammer types, treating this as an image
classification problem. To do so, a dataset composed of 61,800 different images was generated.
The dataset contained different jammer types (including the no-interference scenario), as well as
randomly generated parameters for these interference types. The parameters were modeled following
uniform distributions in order to contemplate a wide amount of different scenarios. The set of
generated images was used to train two different ML algorithms: an SVM and a CNN.
The results showed that with a small library of images and not excessively complex
parameters/network layer architectures, we could achieve a pretty high mean classification accuracy
of more than 90%; specifically, 94.90% and 91.36%, for
SVM
and
CNN
, respectively. In addition,
the interference and interference-free scenario accuracy classification was close to 99%, for
SVM
and
CNN
, respectively. The highest classification accuracy using both
SVM
and
CNN
was when the pulsed
jammer was applied. Pulse jammers were correctly classified with an accuracy of more than 99%.
This happened because pulse jammers had a spectrogram image more dissimilar with respect to
the rest of the scenarios, as can be observed comparing Figure 6e with the rest of the sub-figures in
Figure 6. For its part, one of the lowest classification accuracies, even though it was more than 90%,
was for
AM
and
FM
jammers. On the contrary, as happened with the pulse jammer,
AM
and
FM
jammers’ spectrograms looked very alike, as can be observed comparing Figure 6b and Figure 6d. In
both cases, the most representative feature was one (or sometimes more than one) horizontal straight
line, corresponding to the specific
CW
frequency tones. For its part,
NB
jammers were much better
classified using SVM than CNN, at least with the specific architecture we used for the CNN.
To summarize, we can say that given the low amount of images used for training the algorithms
and given the low complexity used during training, surprisingly good results could be obtained.
Further improvements can be focused on increasing the complexity of the training layers so the
classification can be more accurate; especially to distinguish
AM
and
FM
interferences much better, in
addition to including and trying to differentiate between single and multi-tone
AM
and
FM
jammers.
Author Contributions:
R.M.F. was the main writer of the manuscript, implemented the simulator, developed the
theoretical modeling, optimized the parameters, and performed the simulation based analysis. A.d.l.F. helped in
the research problem definition and in designing the concept. E.S.L. contributed the idea of image-like processing
and of using machine learning algorithms and helped in the algorithm selection and parameter optimization.
Conceptualization, A.d.l.F. and E.S.L.; data curation, R.M.F.; formal analysis, R.M.F.; funding acquisition, A.d.l.F.
and E.S.L.; methodology, R.M.F. and E.S.L.; project administration, A.d.l.F. and E.S.L.; software, R.M.F.; supervision,
E.S.L.; validation, R.M.F.; visualization, R.M.F.; writing, original draft, R.M.F. and E.S.L.; writing, review and
editing, A.d.l.F. and E.S.L.
Funding:
This work received funding from the SESAR Joint Undertaking under the European Union’s Horizon
2020 research and innovation program under Grant Agreement No. 783183 (this project is a partnership
between GMVInnovating Solutions, Tampere University, and the LINKSFoundation; more details at:
https://www.sesarju.eu/node/3107). The opinions expressed herein reflect the authors’ views only. Under no
circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information
contained herein.
Conflicts of Interest: The authors declare no conflict of interest.
List of Acronyms
AWGN Additive White Gaussian Noise
AM Amplitude Modulated
AWGN Additive White Gaussian Noise
BoF Bag of Features
BoW Bag of Words
CDMA Code Division Multiple Access
C/N0Carrier-to-Noise Ratio
CNN Convolutional Neural Network
ConvNet Convolutional Neural Network
CW Continuous Wave

Sensors 2019,19, 4841 14 of 15
CWI Continuous Wave Interference
DME Distance Measurement Equipment
DL Deep Learning
FM Frequency Modulated
FFT Fast Fourier Transform
GNSS Global Navigation Satellite System
GPS Global Positioning System
IF Intermediate Frequency
JSR Jammer-to-Signal Ratio
ML Machine Learning
NB Narrow Band
QP Quadratic Programming
RBF Radial Basis Function
RFI Radio Frequency Interference
RGB Red Green Blue
SMO Sequential Minimal Optimization
SVM Support Vector Machines
WB Wide Band
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(CC BY) license (http://creativecommons.org/licenses/by/4.0/).