Methods for locating signal jammers with a UAV

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Department of Science and Technology

Institutionen för teknik och naturvetenskap

Linköping University

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LiU-ITN-TEK-A--18/027--SE

Methods for locating signal

jammers with a UAV

Andreas Höggren

Love Lindmark

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LiU-ITN-TEK-A--18/027--SE

Methods for locating signal

jammers with a UAV

Examensarbete utfört i Elektroteknik

vid Tekniska högskolan vid

Linköpings universitet

Andreas Höggren

Love Lindmark

Handledare Magnus Karlsson

Examinator Qin-Zhong Ye

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Abstract

Wireless communication today is a modern way to transport data from one location to another. One of the drawbacks of this feature is that a signal jammer can disrupt communications between the receiver and transmitter since the radio waves travel in the open air. This drawback can be exploited in both military and civilian applications.

This thesis will aim on how to locate this kind of transmitting signal jammer over an open field using an Unmanned Aerial Vehicle (UAV) that searches the designated area with the assumption of line of sight between the UAV and the transmitting jammer. Two different methods will be investigated with different techniques, antennas and flight patterns.

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Acknowledgments

We would like to thank FOI for giving us the opportunity to do our master thesis at FOI at the department of Radio Electronic Warfare Systems. We would like to thank our supervisors Leif Festin and Henrik Eriksson and their colleagues Patrik Hedström, Per Brännström, Greger Hast, Anders Johansson, Rolf Jonsson, Joakim Rydell, Michael Tulldahl and all the other great minds at FOI. We would also like to thank our supervisor Magnus Karlsson and our examiner Qin-Zhong Ye at Linköping University for their time.

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List of Figures

1 Radiation pattern and appearance for the Katherein diple antenna . . . 5

2 Flight principle of the method 1. . . 7

3 Localization algorithm plot in MATLAB . . . 8

4 Principle of the triangulation method. . . 9

5 UAV with the mounted directional antenna . . . 9

6 Example of the 3/4 dB method . . . 10

7 Comparison of a clean and noisy rotation measurement . . . 10

8 Different antenna radiation patterns . . . 15

9 Cross-polarization from test chamber investigation . . . 15

10 The user interface in MATLAB while simulating . . . 17

11 Improvised outdoor test . . . 18

12 UAV in the anechoic test chamber . . . 19

13 3D radiation plot . . . 21

14 Dipole rotation test without extra absorbing material . . . 22

15 Comparing dipole rotation tests with and without absorbing material. Elevation angle 10◦_. _{. . . .} ₂₂

16 Comparing dipole rotation tests with and without absorbing material. Elevation angle 45◦_. _{. . . .} ₂₃

17 Dipole antenna elevation sweep . . . 23

18 Final modification for the dipole antenna . . . 24

19 Directional antenna elevation sweep . . . 24

20 EMC chamber test with the directional antenna rotated on full rotation in azimuth 25 21 The directional antenna and installation on the UAV . . . 27

22 Radiation patterns of the directional antenna . . . 27

23 The appearance of the dipole antenna . . . 28

24 MikTran . . . 28

25 Rasberry Pi 3 Model B. . . 29

27 Block diagram of UAV hardware . . . 32

28 GPS positions from the first flight . . . 33

29 The UAVs antenna equipment for the first flight . . . 33

30 The fixed position of the dipole antenna for the second flight . . . 34

31 GPS positions from the second flight with dipole antenna . . . 35

32 GPS positions from the second flight with directional antenna . . . 36

33 UAV with the mounted directional antenna . . . 36

34 The two flight routes done on the 7th of June . . . 37

35 Directional antenna RSSI drop graph compared to Friis . . . 39

36 MikTran GPS test . . . 40

37 UAV RSSI from the second flight . . . 41

38 Polar plot of yaw-data heading . . . 41

39 Triangulation test at FOIs courtyard . . . 42

40 Third flight triangulation results . . . 43

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Abbreviations

Notation Description

ADC Analog-to-Digital Converter API Application Programming Interface ARM Advanced RISC Machine

CCW Counterclockwise CW Continuous Wave

EMI Electromagnetic Interference EW Electronic Warfare

FOI Swedish Defence Research Agency GPS Global Positioning System

GUI Graphical User Interface LIDAR Light Detection And Ranging LOS Line Of Sight

LS Least-Square

MAVLink Micro Air Vehicle Link MP Mission Planner

PDF Probability Density Function PDOP Position Dilution of Precision RF Radio Frequency

RSSI Received Signal Strength Indication SDR Software-defined Radio

SNR Signal to Noise Ratio SoC System-on-a-Chip SSH Secure Shell

UAV Unmanned Aerial Vehicle UDP User Datagram Protocol

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1 Introduction

This master thesis is performed for the Swedish Defence Research Agency (FOI) at the department of Radio Electronic Warfare Systems. This section describes the background of signal jammers and this master thesis goals and limitations.

1.1 Background

Wireless communication today is expanding fast as the need for a connection between larger dis-tances for communicating systems or individuals is essential. This thesis work will investigate if a UAV equipped with the proper equipment can localize a signal jammer that transmits a known frequency and a constant radiated power.

1.1.1 Signal jammers

A signal jammers purpose is to disrupt a communication between two communicating devices. A signal jammer is a device that’s being used to transmit a signal that has the same frequency as the wireless communication system which is the victim of this operation.

1.1.2 Usage of signal jammers

The catch with this type of communication is that the wireless version travels through the air, which leaves the traveling signal vulnerable to interference. This interference is an electromagnetic interference (EMI). Signal jammers are often used in combat zones to disrupt communications of the opposing force. An example of this is the Russian developed vehicle Borisoglebsk-2 which disrupts wireless HF, UHF and mobile communications [1]. This is called Electronic Warfare (EW) and this vehicle has been involved in the annexation of Crimea in Ukraine [1].

There are other cases than military purposes, an example of a civil case is the Swedish police in Stockholm, Sweden, reports illegal use of signal jammers by thieves to block the signal from the car keys, leaving the vehicle unlocked and therefore easy to break into [2].

1.1.3 Usage of multirotor drones

The different applications of UAVs in terms of drones, has evolved fast through the years, both from a military and civil perspective. Military use of multirotor UAVs is rapidly growing. Since they are light, relatively cheap and easy to use they are perfect for tasks such as reconnaissance, sending or receiving signals from a higher ground, and even for reducing morale by sending propaganda and misinformation. UAVs has through the years performed certain deliveries from one place to another, companies such as Amazon is involved in this area among others. Another example is that Apple has an idea in how to improve Apple Maps after a several year long struggle to get the application to work, which is to let drones capture aerial images [3].

1.2 Goals

The goal of this master thesis is to investigate different UAV-related methods for determining the position of a signal jammer, which in this scenario is a stationary continuous wave (CW) signal source. The transmitting signal is at a fixed frequency and an unknown power.

As a summary, this thesis will cover:

• Performance investigation of antennas and considerations.

• Development of a simulation environment to test positioning methods. • Evaluation and comparison of signal source positioning methods.

1.3 Limitations

Some limitations are present in this thesis, such as implementation requirements placed by FOI. These are described in detail below.

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Signal jammer

A signal generator is used to generate a sine CW signal at a frequency of 1877 MHz and 17 dBm radiated power which is transmitted through a dipole antenna. A weaker signal shrinks the region where the UAV can detect and find the signal jammer accordingly.

UAV Platform

An existing UAV platform is to be used with some limitations regarding weight and physical size of the payload. The flight controller is a Pixhawk Mini [4] and any communication with it is therefore limited to the Micro Air Vehicle Link (MAVLink) interface [5].

Receiver

One of the requirements of the master thesis is that the receiver uses a Software-Defined Radio (SDR) such as FOI’s MikTran platform.

Since the UAV platform is already designed, no fabrication is required. The antennas for the thesis are either already acquired or bought from third-party sources. The hardware platform that will perform the recordings from the UAV deck is also already developed and is called MikTran. The hardware part will therefore mostly consist of choosing appropriate antennas and mounting them with minimal interference.

1.4 Related work

There has previously been similar research regarding this field where a UAV is equipped with antennas in order to acquire received signal strength indication (RSSI) data and give heading estimations. UAVs has also been equipped with other gear than antennas, such as 3D Light Detection And Ranging (LIDAR) sensors to investigate points of interest.

1. Körner et al. [6] discusses the improvement of locating radio tagged wild animals by a UAV with the mounted directional antenna. Their method offers a number of advantages compared to search from the ground, including better line-of-sight (LOS) signal reception, terrain-independence and faster localization.

2. FOI has performed projects by using UAVs several times. One of them consist of evaluating the benefits and capabilities of high resolution 3D data by a UAV equipped with LIDAR. Application examples is a way to fast perform a geometric documentation of an interesting area [7].

1.5 Method

This thesis work process can be described by the following five steps: Background

A theoretical background is an essential vantage point of the understanding in how Radio Frequency (RF) behaves in the environment and this thesis aim of focus. Therefore a background study regarding this field is necessary, see Chapter 2.

Theory

Includes theory for antenna radiation patterns and equations regarding wireless communication systems. This is presented in Chapter 3 and Chapter 4.

Implementation

When the pre-study phase is passed, implementation of algorithms and equations are added to the MATLAB Graphical User Interface (GUI), and are tested in real flights. See the relevant Chapter 3, Chapter 5, Chapter 6 and Chapter 7.

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UAV flights

Every UAV test flight during the entire thesis project is presented in Chapter 8. It covers informa-tion regarding UAV equipment, flight patterns as different methods of localizainforma-tion and thoughts of improvement.

Results

When the UAV-simulation GUI in MATLAB is advanced enough, the received power characteristics will be compared of that of an actual flight with a signal jammer in the field. The UAV will be tested with different antennas and give position estimations. The RSSI measurements are presented in Chapter 9 and discussion regarding the results are presented in Chapter 10 as conclusions. Finally some thoughts regarding improvements as future work are presented in Chapter 11.

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2 Radio jamming interference

Radio jamming occurs when a transmitting device gets overpowered by a signal jammer that is tuned to the same frequency as the targeted transmitter. The receiving antenna will then be interfered and therefore the communication between a receiver and transmitter is disrupted.

2.1 Different jamming methods

There are several kinds of signals when it comes to signal jamming, for example continuous wave signals and modulated signals. A continuous wave is defined as a constant frequency and power as a simple sine wave while modulated signals vary in amplitude or frequency. Modulated signals can therefore be harder to detect since the jamming signal may appear as a regular communication to a receiver. This thesis will focus solely on localization of CW signal jammers.

2.1.1 Frequency-hopping and sweeps

Frequency-hopping is a method for transmitting a signal at different frequency bands by adjusting the carrier frequency at a programmed pace and in a pseudorandom order. The order of frequency shifts are pre-programmed for both transmitter and receiver. This is a way to avoid a communica-tion being interrupted or eavesdropped by a third part. There are frequency-hopping systems that behave more like a frequency sweep rather than a random pattern. These electronics has a usage for civil purposes while more advanced systems like military radios attempt a more pseudorandom pattern [8]. However, there are jammers that counter this sort of techniques and solves the systems random pattern algorithm.

2.2 Jammer for field tests

The signal generator that’s being used to generate a Continuous Wave (CW) jamming signal is the R&S SMC100A signal generator [9]. The signal generator transmits a sine-wave signal at 1877 MHz with a transmitted power of 17 dBm. The R&S signal generator is connected to a Katherein 738 449 dipole antenna mounted vertically 1.5 meters above ground on a tripod [10]. See Figure

1afor the jamming antennas radiation pattern along the vertical plane. The appearance of the jamming antenna is presented in Figure1b.

(a) (b)

Figure 1: The Katherein dipole antenna. (a) The radiation pattern of the Katherein antenna that’s being used in combination with the R&S signal generator [10]. (b) The Katherein antenna that can be mounted on top of a tripod for field tests [10]

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3 Localization methods

Two different methods for localizing the jammer were evaluated.The first method uses a dipole antenna to provide RSSI data, while the second method uses a directional antenna instead. Both of these methods require a certain flight navigation to determine a possible signal source location. The reference as a true location of the signal jammer was with a smartphones Global Position System (GPS) coordinate by the jammer. This method of the true jamming location was used for all flights.

3.1 Method 1: RSSI using dipole antenna only

The first method of jammer localization was the dipole antenna as receiver that was mounted directed downwards underneath the UAV and the UAVs flight pattern was similar to a wide zigzag pattern across the field of interest. See Figure2. The idea of this method is that it could provide an exact position if the general area the jammer is located in is known. Two different dipole antenna mountings were evaluated in this thesis. The satellite imagery of FOIs courtyard are acquired from Eniro [11].

Figure 2: The marked cross represent an example of the signal jammer location while the white drawn line illustrates the flight pattern of the first method.

This method has previously been used for indoor localization and uses the optimization algo-rithm from [12] which estimates the source position based on the estimated distance to the source at a number of anchor nodes. This algorithm is from here on called the unconstrained least-square (LS) estimate. In this case, the anchor nodes is the UAV’s positions along the flight path and the estimated distance is the RSSI value at each position. Equation1 gives the estimated position q as a 2 × 1 matrix. q = 1 2(AS) + At, A = S′_P wW Pd (1)

Where (AS)+ _{is the pseudoinverse of AS, P d is I(N − 1) −} dd′

d′d, S is a 2 × N matrix of all

position coordinates, W is I(N − 1), t is R2

−d2_{, R is the baseline distance of all N positions}

from the first position, d is the reported distance (RSSI) difference of all positions from the first position and I is the identity matrix.

It is not possible to make absolute distance estimations from RSSI values if the jamming signal strength is not known, since a received signal of a particular strength could be a weak signal from a short distance or a strong signal from a long distance. The received power is the key element when estimating the jammer location. There are many kinds of algorithms regarding estimations of a signal source when using several sensors or anchor nodes as they also are called. These anchor nodes will correspond to the UAVs measurement points during flight. From the received signal

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strength variation the distance variation can be estimated and the location of said estimation can be found from the GPS-coordinate log. See Figure3 of a MATLAB simulation of the algorithm.

Figure 3: The unconstrained LS estimate with 25 measurement points. The jammer is located with perfect precision since all distance measurements are ideal.

3.2 Method 2: RSSI positioning using directional antenna only

For the second method, the UAV is equipped with a directional (panel) antenna mounted un-derneath at a vertical angle of 39◦_{. A vertical angle between 30}◦ _{to 45}◦ _{is desired to make the}

directional antenna able to receive signals from a large distance span with minimum attenuation. While logging the current heading (yaw) from the flight-controller as well as the GPS-position and received signal strength from MikTran, the UAV rotates one full rotation along its yaw-axis while remaining stationary. The Analog-to-Digital Converter (ADC) on the MikTran platform measures 1500 RSSI values approximately 8 times a second and logs the max value of these 1500 values. The Raspberry Pi 3 supplies the current UAV heading at a sample rate of 10 samples per second. The yaw-data gives coordinates from 0◦_{to 359}◦_{, where 0}◦_{is North and 90}◦ _{is East and so on,}

all related to true North. Flying to a second point perpendicular to the direction of the jammer and performing another yaw-rotation will give another jammer direction. These two directions will intersect at the jammers position. See Figure4of the triangulation method.

Since the directional antenna will be the first area of contact to the ground when performing landings, a set-up of boxes were placed in a square on the ground for the UAV to land flatly on to avoid tilting and break any of its propellers. See Figure5 for the mounted directional antenna while the UAV is airborne.

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Figure 4: This is the principle of the triangulation method that is described. The UAV will commence its flight by performing one full rotation while receiving RSSI data from the directional antenna. The UAV will then navigate towards a second coordinate and perform the same rotation once more. The crossing of the two RSSI headings will give the jamming location.

Figure 5: UAV with the mounted directional antenna while the drone is airborne at a low altitude. The UAV will perform a full rotation in the air at an average rotation speed of approximately 20◦_{/sec and save the heading which receives the highest RSSI. The rotation pace of the UAV is}

set to the lowest possible by the UAV pilot. When the rotation is accomplished and one heading noted, the UAV will travel to another GPS coordinate perpendicular to the heading found in the first rotation and repeat the full rotation scan. These two lines with their respective location and direction will eventually cross at the jammers approximate location. This kind of localization method fall under the triangulation category and is a typically known localization method in position estimations from RSSI.

3.2.1 Estimating the jammer direction

Ideally, the heading of the maximum RSSI will be the direction to the jammer since the radiation pattern of the directional antenna is strongest in the straight forward direction. In reality, this peak can be found anywhere between −30◦ _{to +30}◦ _{of the main lobe. A more reliable method is}

to use the mean or median between the two angles where the signal is -3/-4 dB lower than the max, since the main lobe is much wider and more defined than the absolute peak [13]. See Figure

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Figure 6: Example of the various methods of estimating the direction of the jammer. Note that the max measurement direction differs quite a lot from the true direction and that the -3/-4 dB method provide better accuracy.

3.2.2 Filtering out noisy rotations

While the UAV is airborne and performing a full rotation, wind can cause it to tilt, rotate at uneven speed and gain or lose altitude rapidly. Therefore some of the rotations may become noisy and as a result provide less accurate estimates of the jammer’s direction. By setting a maximum allowed RSSI variance of at least 5 in the -4 dB region the noisy and inaccurate rotations can be filtered out. See Figure7.

(a) (b)

Figure 7: Comparison of a clean rotation measurement and a noisy rotation measurement. (a) The clean rotation. (b) The noisy rotation, which is filtered out during jammer location estimations because of the large variance found in the -4 dB region.

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3.2.3 Triangulation ground test

While developing the triangulation method, a series of ground tests were performed in the court-yard. The triangulation method with the directional antenna mounted on the UAV was tested on FOIs courtyard with the UAV held at approximate 1.5 meters above ground. The jamming dipole antenna transmitted the CW signal while the UAV was displaced on foot at different locations and rotated at a slow rotation speed. The RSSI-data is then evaluated in MATLAB to investigate if the maximum power peak is having the correct bearing towards the jamming source. The evaluation of the RSSI data are presented in Chapter 9.3.1.

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4 Radio propagation theory

Background knowledge for multipath propagation is needed for the simulation environment as well as for understanding the measured results. This chapter will describe some parameters that are included in our simulations for the measurements in radiated power.

4.1 Attenuation

The power reduction based on the distance in free space is know to be 1/R2_{path loss factor from}

Friis equation. Reflected signals from the terrain may increase the path loss to 1/Rn_{. Where the}

potency n can vary from 2 to 5 or 6 when exposed to many lossy obstructions [14]. The decrease of signal power caused by losses in propagation paths is called attenuation. This loss depends on the distance between receiving and transmitting antennas, interference, multipath propagation and reflections for this thesis. Other factors include atmosphere and precipitation [14].

4.1.1 Friis equation

Friis equation expresses the received signal as a function of transmitted power, antenna gain, range and frequency and is essential in wireless communication systems. See Equation2 for the power density radiated by an isotropic antenna. Ptis the transmitted power and R is the distance between

the two antennas. The power density Savgis defined in W/m2and is the free space path loss [14].

Savg=

Pt

4πR2 W/m 2

(2) The received power, Pr to the receiving antenna can be derived from Friis equation. Ae is the

effective aperture area of receive antenna, it can also be called as "capture area". See Equation3. Pr= AeSavg=

GtPtAe

4πR2 W (3)

4.2 Reflection

Transmitted electromagnetic waves have a tendency to have its signal reflected, which leads to that the signal takes different paths to the corresponding receiver. This phenomenon is depending on what kind of surface the signal reaches. A communication on a short distance will most likely be affected by metallic objects or big environment objects surfaces, such as buildings. Wet objects such as oceans provides reflection at longer ranges [15].

4.3 Multipath propagation

Multipath propagation is a phenomenon that occurs when signals reflect on various obstacles in the environment which causes reflected signals to take multiple paths to the receiver, and therefore arrive with different delays and signal strengths. This can cause constructive or destructive inter-ference depending on the path delays of the environment. Destructive multipath propagation is called multipath fading [16]. Accurate multipath propagation simulation requires a detailed map of the surrounding landscape and a lot of computational power, however there are a couple of statistical approximations which are quicker to calculate. For LOS-signals, Rician fading is the best fit.

4.3.1 Rician fading

For LOS signals, the fading can be statistically modeled by Rician channel fading which is based on the Rician probability density function (PDF). The Rican fading model takes a dominant signal wave into account, typically the LOS signal wave since it has no blocking obstacle. The signal is affected by reflections from the environment and can cause constructive- or destructive interference. I0 is the zeroth-order of the Bessel function of the first kind. See Equation4 [17].

f (r) = r σ2exp (− r2_{+ k}2 d 2σ2 )I0 rk2 d σ2 , r ≥ 0 (4)

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Equation5 can be used to estimate the Rician K-factor, where A is defined as the amplitude given for an incoming signal, I1 is the first order modified function of the first kind, E[A] is the

average amplitude and E[A2_{] is the average of the amplitude squared [}₁₇_].

E[A] E[A2_] = r _π 4(K + 1)exp (− K 2 ×[(K + 1)I0( K 2) + KI1( K 2)] (5) 4.3.2 Rician K-factor

The K-factor describes the Rician distribution as the ratio between deterministic signal power (LOS) and indirect paths. The amount of fading can be estimated in dB. See Equation6[17].

K(dB) = 10log10

k2 d

2σ2 (6)

kdis a constant that relates to the direct components strength and σ is the standard deviation.

The K-factor can also vary if the receiver is in motion relative to the transmitter. But this is neglected since the UAV will keep a low velocity.

4.4 Received Signal Strength Indication

RSSI is a relative measurement of received power and indicates the signal strength between a transmitter and receiver. The RSSI has an important role regarding this thesis since these mea-surements can be used for the localization of the signal source. The Signal to Noise Ratio (SNR) is useful for RSSI estimations when running background noise to the simulation environment of the GUI. It gives the ratio of the signal energy and noise energy level. See Equation7 [18].

SN R(dB) = 10log10

Psignal

Pnoise

(7) The SN R(dB) is a value in decibels, Psignal determines the average power of the signal and

Pnoise is the average power of the noise.

4.5 Radiation pattern

The field strength pattern of a transmitting or receiving antenna can be illustrated in a plot based on either Cartesian coordinates or polar coordinates. In this thesis the radiation patterns are shown in polar plots. These plots gives information on how the radiation expands in gain (dBi) for different directions around the antenna and these areas are named lobes. A low gain antenna will typically emit a radiation pattern uniformly in all directions, while a high gain antenna will focus its main lobe in a particular direction. The lobe(s) with the highest gain is called the main lobe and most of the antennas radiated energy will be directed in this direction. Good examples of high- and low-gain antennas are the directional- and dipole antennas respectively. See Figure

8aof a dipole antenna’s radiation pattern and8bof a directional antenna’s radiation pattern. The 3D pattern illustration of the directional antenna is based on the given H- and E-fields from the distributors data-sheet [19], see Figure22.

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(a) (b)

Figure 8: The simulated 3D radiation pattern plots of the two antennas used on the UAV. Both are drawn with the same point of view with a 27◦_{elevation rotation and both are vertical polarized and}

the colorbars represents the power in dB-scale. The simulations are from the MATLAB toolbox kit. (a) The dipole antenna. (b) The directional antenna.

4.6 Polarization

Two communicating antennas should both keep the same placement related to polarization in order to avoid any cross-polarization that drastically reduces the received power [14]. If one of the two antennas is placed sideways, cross-polarization occurs [20]. Cross-polarization is investigated in the anechoic test chamber at FOI as a test. See Figure9 of a case when cross-polarization occurs as the antennas both are vertically polarized but the receiving directional antenna is turned 90 degrees sideways and therefore affects the performance of receiving signals as the elevation sweep goes approximately 90◦_{. It is important that the receiving antenna is not cross-polarized with the}

transmitting antenna or the signal will be extremely attenuated.

Figure 9: This is a plot of the directional antenna that is placed 90◦ _{sideways. The directional}

antenna is then rotated approximately 90◦ _{in elevation. The negative peak that decreases in}

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5 Simulation and measurement environments

A simulation environment in MATLAB is made before a real flight with a UAV and its equipment. This is done to test localization methods before trying them in real flight, e.g. predict which flight route gives the best performance in the shortest time.

5.1 Introduction to the user interface

The GUI-field is a landscape of size 500x500 pixels that has a jammer location given and a UAV that moves accordingly to a pre-programmed route or by dragging it with the mouse cursor, see Figure 10. The jammer and UAV also have given heights relative to the ground. The jammer is affected by its radiation pattern to determine how much of the peak power is radiated to the UAV’s coordinate. The amount of received power also decreases quadratically with the distance between jammer and UAV. This fading pace is due to Friis equation, which says that the received power decreases with 1/R2_{, where R is the radial distance from transmitting antenna [}₁₄_].

Figure 10: The simulation GUI with a heatmap that shows the radiation pattern of the jammer. In this case the jammer is equipped with the Kathrein dipole antenna. The dots ahead of the UAV defines its heading. In the title text the position, received power, distance, antenna fading and angle of the UAV is shown.

5.2 Antenna profiles

Since the UAV is moving around the jammer in all three dimensions, the antenna radiation patterns must be taken into account to simulate the signal strength accurately. This was done by calculating the angle between the UAV and the jammer in the ZX-plane and the XY-plane and then referencing the antenna’s radiation pattern strength in that direction.

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5.3 Radiation pattern for the UAV platform and antennas

An investigation in order to find out if the UAV platform affects the RSSI measurements via reflections on the dipole and directional antennas mounted on the UAV is described here. The first improvised test was performed outdoors in FOI’s courtyard and later on a more professional test is performed in an anechoic test chamber at FOI.

5.3.1 Reflection test at FOIs courtyard

A basic test was performed outdoor in a partially open field, rotating the UAV on a short distance of approximately nine meters from the transmitting jammer with bare hands. By using a hand-held R&S FSH3 spectrum analyzer, the RSSI strength proved to deviate from different azimuth (horizontal) angles while the R&S signal generator used the same settings as it would during a real flight [21]. The azimuth angle was rotated at 45◦ _{increments as the spectrum analyzer measured}

the received signal peak. A signal reflection test in the anechoic test chamber is necessary in order to reduce undesired noise and investigate the antenna further. See Figure11of the set-up.

Figure 11: The improvised outdoor test where the performance of the dipole antenna mounted on the UAV was evaluated with a spectrum analyzer. The UAV is held upside down.

5.3.2 Anechoic test chamber tests

FOI in Linköping owns two anechoic test chambers and one of them is used to investigate the reflections from the UAV to the dipole antenna on board. The UAV is placed on a table that can rotate automatically both in elevation (vertical) and azimuth angles, while a signal generator delivers a signal for the UAV to receive. The Anritsu M54644A network analyzer will be used to log the measured power for a certain angle on the table for the antennas separately on the UAV. The rotation sweeps are rotated counterclockwise (CCW). The tests in the anechoic chamber took place in the 26th of April 2018. A Yagi-antenna was acting as the jammer during this test and sends CW signals vertically polarized from 700 MHz to 3000 MHz for every angle while the table that is six meters away. The antennas on the UAV are connected to the network analyzer to record the received power data. The antenna was placed on the table upside down in order to acquire the correct relation in angles between the receiver and transmitter, as it would during a real flight. This test performed RSSI measurements when the UAV on the table was fix at 10◦

and 45◦_{respectively, while performing a full rotation in the azimuth plane. Elevation sweeps were}

also performed but with a limitation to approximate 100◦ _{span. See the mounting of the UAV in}

Figure12.

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Figure 12: The UAV mounted upside down in the test chamber with the dipole antenna attached clearly visible. The table on which the UAV is placed could rotate in both azimuth- and elevation angles.

The received signal with the dipole antenna attached while the UAV rotated at different ele-vation angles can be seen in Figures14, 15 and 16in Chapter 6. There is a possibility that the UAV platform reflects the incoming signal and affects the received radiated power on the dipole antenna. This ripple fades somewhat when the UAV rotates so that the dipole antenna is closer to the signal source than the UAV platform.

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6 Reflection measurements

This chapter presents the measurements from the anechoic test chamber for both UAV antennas at FOI.

6.1 Dipole antenna measurement

This section describes the simulations from anechoic test chamber experimentation, when eventual reflection occur by the UAV platform from the transmitted signal to receiving dipole antenna. The network analyzer in the test chamber generated a signal of 700 MHz to 3000 MHz for every measurement angle on the rotating table on which the UAV was placed. See Figure13 for the simulation of the entire 700-3000 MHz frequency spectrum of the dipole antenna reflection test.

Figure 13: Three dimensional plot for the dipole antenna with a fixed elevation angle of 10◦ _from

the anechoic test chamber across the entire frequency span of 700-3000 MHz with corresponding rotation angles in azimuth and RSSI values.

All the measurement graphs from the anechoic test chamber are plotted at 1875 MHz frequency since the network analyzer transmits the CW with an increment of 5 MHz. The dipole antenna is used to receive the transmitted signals in order to investigate any reflections. See Figure 12

for UAV placement on the rotating table. The reason why the UAV was placed upside down on the table is because that it gave the desired relation in angles between transmitting and receiving antennas just as in an actual flight. Figure14shows the reflection behaviour after the UAV with the dipole antenna was rotated one full rotation.

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Figure 14: UAV rotated 360◦ _{along the azimuth axis while the elevation was fixed at 10}◦ _{and 45}◦

respectively. The frequency was set to 1875 MHz.

Due to the ripple in Figure14, a new test was performed in the anechoic test chamber, since a metallic frame below the transmitting antenna for another irrelevant project might have affected the previous measurements. Figures15and16shows the comparison between with or without the absorption material.

Figure 15: UAV rotated 360◦_{along the azimuth axis while the elevation was fixed at a 10}◦_elevation

angle. The first test is without any absorbing material covering the metallic frame below the transmitting antenna, while the second test is with the absorbing material applied. The frequency was set to 1875 MHz.

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Figure 16: Same test as in Figure15but with a fixed elevation angle of 45◦_{instead. The frequency}

was set to 1875 MHz.

The purpose of the dipole antenna mounted on the UAV is to receive the transmitted signal re-lated to method one, compared to the directional antenna which works in long range measurements. Since the reflection simulations in the anechoic chamber at FOI proved to give rather unstable graph plots with ripple, the dipole antenna was displaced to another location that wouldn’t risk the same amount of reflections. Figure 17 shows the dipole antennas performance when the azimuth was fixed and the elevation angle was tilted.

Figure 17: This graph shows the received power of the dipole antenna while the rotation table rotated 96◦ _{in elevation. The frequency was set to 1875 MHz.}

The RSSI ripple for the dipole antenna with the different elevation angles is possibly influenced by the UAV platform and the rotating table which is conducting. No further experimentation in what may inflict ripple on the measurements was be performed, since this was not the main focus. To minimize platform interference, the dipole antenna was relocated to a more suitable position for less exposure to reflection. See Figure18for the new dipole antenna location on the UAV.

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Figure 18: This is the final position of the dipole antenna on the UAV. The UAV with the dipole antenna headed downwards at a more central location than previously. This installation was never tested or used in flights.

6.2 Directional antenna measurement

The directional antenna was used for the second methods position estimations. Figure19shows the elevation sweep of the directional antenna.

Figure 19: This graph shows the smooth curve of the received power while the rotation table rotated in elevation from –5◦ _{to 91}◦_{. The frequency was set to 1875 MHz.}

The directional antenna was also rotated along the azimuth axis one full rotation and the RSSI is drawn as a polar plot that can be seen in Figure20.

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Figure 20: Measured radiation pattern of the directional antenna in the anechoic chamber. The frequency was set to 1875 MHz and the scale of the plot is in dBm. The elevation angle was kept constant at 0◦_{while performing the azimuth sweep.}

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7 Equipment for RSSI measurements

This section covers both the hardware and software that are required in order to perform the desired RSSI measurements.

7.1 The directional antenna on the UAV

The directional antenna is the Pro-1000, GSM panel antenna that will be mounted on the UAV is a panel antenna bought from Televes (formerly Macab) and antenna characteristics is given on their website. The frequency spectra is 1710-2500 HMz among others and 1877 MHz was used in the field as well as 1875 MHz for the anechoic chamber tests at FOI. Maximum power is 100 W and the antenna will be connected to the RF input port on the MikTran-platform [19]. This antenna will only be used to receive signals. See Figure21afor the panel antenna, which is called the directional antenna in this thesis and the mounting on the UAV on Figure21b.

(a) (b)

Figure 21: The directional antenna used for triangulating the signal jammer. (a) As it appears on the distributors website [19]. (b) Installed on the UAV.

The radiation patterns are illustrated straight from the data-sheet in Figure22. Note that the vertical radiation pattern corresponds to the 3D radiation pattern of the directional antenna in Figure13. The vertical opening angle is at 55◦_{for the relevant frequency band at 1710-2500 MHz.}

Figure 22: The polar graphs of the radiation patterns as horizontal and vertical respectively. The frequency for the radiation patterns here is at 2100 MHz [19].

7.2 The dipole antenna on the UAV

The provided dipole antenna that is mounted on the UAV has been purchased from Round Solutions and is named Screwable Small Stubby Antenna with 1 Cable 2G/3G [22]. See Figure23.

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Figure 23: The dipole antenna that is installed on the UAV during the flights related to the first method [22].

7.3 MikTran platform

The MikTran platform has an onboard AD9361 ADC, a GPS module and a Xilinx Zynq Advanced RISC Machine (ARM) System-on-a-chip (SoC). The AD9361 Application Programming Interface (API) and the libgps library allows software to run on the Xilinx Zynq with real-time data from the ADC and the GPS module. When this platform is used in the field on the UAV it is sealed inside a metal box that functions both as protection and as a heatsink, see Figure24.

The two receiving antennas is connected one at a time with an SMA connection to the MikTran in order to record its RSSI measurements. A non-magnetic GPS antenna provided by FOI is also connected to the unit on a separate SMA connector in order to pair the correct measurement to the current GPS coordinate of the UAV. The MikTran platform is also connected to a Raspberry Pi 3 using an ethernet cable.

Figure 24: The size of the MikTran compared to a five SEK coin. 7.3.1 MikTran software

The Xilinx Zync SoC onboard the MikTran platform runs a custom application for doing measure-ments, written in C. This application uses the AD9361 API to retreive 1500 RSSI measuremeasure-ments, gets the latest GPS location using the libgps library with the onboard ORG4572 chip from Orig-inGPS, and listens for User Datagram Protocol (UDP) packets from the Raspberri Pi on a separate thread. The applications logs the maximum value of the 1500 RSSI measurements, the latest GPS-coordinates and the latest heading-value that was received from the Raspberry Pi to a text file approximately 8 times a second.

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7.4 Raspberry Pi 3

The UAV platform comes with a Raspberry Pi 3 unit installed, see Figure25. This unit is used to communicate with other hardware present on the UAV and is connected to the Pixhawk flight controller via USB. Over this connection, the MAVLink protocol feeds the Raspberry Pi with constant updates from the Pixhawk flight controller. Since the heading of the UAV is needed for determining in which direction the jammer is relative to the UAV, the heading data from the Pixhawk is forwarded to MikTran via UDP using an ethernet cable between the two.

By Mak e Magazin [CC BY-SA 4.0 (https://creativecommons.o rg/licenses/b y-sa/4.0)], from Wikimedia Commons

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8 Test flights

This chapter contains the outcomes of each test flight with thoughts and improvements for upcom-ing flights.

8.1 UAV equipment

This sub-chapter describes all hardware material and arrangements that are relevant for the flights. The UAV is navigated manually by a pilot from FOI via radio telemetry hardware. Mission Planner (MP) is the interface that gives information directly from the UAV during the flights from different sensors such as altitude above ground. See Table2 for the list of equipment that where present during all flights. The UAVs main platform and arms are made of carbon fibre and designed at FOI.

Table 2: This table covers the equipment that were used during the UAV flights, excluding antennas for RSSI measurements.

UAV Equipment

Specific hardware Model Quantity Vehicle Hardware

Propellers T-Motor CF 15"x4.8" (diameter x inclination) 4 Motors T-Motor kV 580 (MN3508-20) 4 Battery Dynamo LiPo 4S, 14.8 V, 10000 mAh 1 Electronic Speed Controller XRotor-pro 25 A 4 Flight Computer Pixhawk Mini, Arducopter software 1 RC Radio Futaba 1 Vehicle Telemetry

Telemetry 3DR Wifi Telemetry Radio 1 Ground Control Hardware

WiFi router (with external antenna) FOI 1 Computer with PuTTY FOI 1 With the components in Table2, the UAV is estimated to be able to perform flight missions for 18 minutes ±1-3 minutes depending on the wind and temperature in the air. The appearance of the UAV for the thesis project is presented in Figure 26a and Figure 26b. A detailed block diagram of the UAV hardware is shown in Figure27.

(a) (b)

Figure 26: The UAV with attached components, such as MikTran, GPS, dipole antenna, Raspberry Pi and Wifi antenna. Note that the propellers are removed for these pictures. (a) The UAV seen from underneath. The microprocessor MikTran is clearly visible at the centre of the UAV, capsuled in its protective box. (b) The UAV seen from above. This side reveals the strapped on battery at the centre, Raspberry Pi 3 and GPS (upper left arm).

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Figure 27: Block diagram of the UAV system hardware.

8.2 First flight

The first test flight took place on the 27th of March 2018 in an open field environment on the countryside of Linköping. The aim of this test flight was to measure radiated power data from a stationary dipole antenna while the UAV was controlled manually by a pilot who took the flight route as seen in Figure28. The satellite image was taken from Eniros website and the dots were placed using the MATLAB mapping toolbox and colored based on the received signal strength [23].

The MikTran was connected to a small dipole antenna hanging from underneath the UAV dur-ing flight and communications with the MikTran on-board the UAV was done via a Secure Shell (SSH) tunnel over Wi-Fi. During this flight the UAV began the flight by increasing its altitude to approximately 20 meters above ground and then it flew according to the plot in Figure28. In the end of the flight above the jammers location the altitude was decreased to a few meters above ground. The LOS between the antennas lasted during the entire flight.

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Figure 28: The recorded data of GPS coordinates by the GPS antenna stored in the MikTrans memory. The dots show the flight path of this test and the color indicates the measured radiation strength (white - strong signal, red - weak signal).

The thoughts for the next flight were to increase the sample rate of the MikTran and to install the dipole antenna in a fixed position so that it does not hang freely. The appearance of the UAV during its first flight can be seen in Figure29.

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8.3 Second flight

The second test flight took place on the 20th of April 2018. The altitude was decreased to approx-imately 13 meters above ground due to strong winds above the surrounding treetops. Figure30

shows the new, fixed mounting of the dipole antenna. The dipole flight route is shown in Figure

31.

Figure 30: The dipole antenna steadily mounted on one of the UAV arms is facing downwards. The UAV landed on the ground after the flight with the dipole antenna so that the dipole antenna could be replaced by the directional antenna. The flight route with the directional antenna can be seen in Figure32 and Figure33illustrates the set up with the directional antenna below the UAVs platform. Since the directional antenna under the UAV is the first area of contact when the UAV performs a takeoff or landing, four plastic boxes were placed under each arm as a takeoff-and ltakeoff-anding-pad.

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Figure 31: The recorded data from the flight stored in the MikTrans memory. The dots show the flight path of this test and the color indicates the measured received signal strength with the dipole antenna (white - strong signal, red - weak signal).

For the test with the directional antenna were four different 360-degree rotations performed at different locations ranging from approximately 30 meters up to 180 meters away from the signal jammer. The signal generator that powered the signal jammer was turned off between the rotations when the UAV was moving to the next position. The signal jammer was left transmitting the entire time during the flight back towards the signal jammer after the fourth spin.

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Figure 32: The flight route with the directional antenna. The UAV performed four rotations at the different white rotation markings. The distance away from the jammer at the different locations are I) 32.12, II) 52.2, III) 108.0 and IV) 180.8 meters.

Figure 33: UAV with the mounted directional antenna while the drone is airborne. Thoughts after the second flight was to change localization method rather than modify the algorithm that uses RSSI data based on the dipole antenna only. The reason to change the localization method is because that the RSSI measurements gave two peaks rather than one that was desired. See Figure37 of the RSSI (dBm) data plotted in a 3D graph where the x-axis and y-axis is the landscape of the field area.

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8.4 Third flight

The third test flight took place on the 7th of June 2018. Due to unstable wind conditions, the altitude was held at approximately 13 meters just as the second test flight in April. Only the directional antenna was installed during this flight since the purpose was to test the triangulation method. The two flight routes are shown in Figure34.

(a) (b)

Figure 34: The two flights done on the third flight day. The first route is shown in (a), the second route in (b).

The UAV performed two flights where the first one consist of seven azimuth rotations and the second flight of five rotations. Two flights were performed due to strong wind and unstable rotations during the first flight but unfortunately the second flight had similar conditions. The performance of the RSSI measurement is affected by how much the UAV sways and how stable the altitude can be kept when the azimuth rotations are performed.

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9 Results

In this chapter the performance of the antenna equipment, GPS positioning and the different localization methods will be presented.

9.1 Antenna performance

This sub-chapter will go through the performance of the two antennas mounted on the UAV. 9.1.1 Dipole antenna

Since the reflection simulations in the anechoic chamber at FOI proved to give rather unstable graph plots with ripple, the dipole antenna was displaced to another location that wouldn’t risk the same amount of reflections, see Figure18. Figure 17shows the dipole antennas performance when the azimuth was fix and the elevation angle was tilted.

The RSSI ripple for the dipole antenna with the different elevation angles is possibly influenced by the UAV platform and the rotating table which is made of metal. No further experimentation in what may inflict ripple on the measurements was be performed since it wasn’t the main focus of the thesis. However, the name of the dipole antenna was provided after the anechoic chamber measurements. The manufacturer at Round Solutions doesn’t have a radiation pattern on their website for this dipole antenna. Other websites has similar antennas of the same appearance and the radiation patterns provided shows a unstable pattern for similar frequencies [24]. This means that the RSSI ripple for the dipole antenna in the anechoic chamber could also be affected by the receiving antennas radiation pattern.

9.1.2 Directional antenna

The directional antenna was not affected meaningfully by any reflections during the test in the anechoic chamber, as seen in Figure19. The measured radiation patterns of the directional antenna had no significant ripple or discontinuities as seen in Figures 19and 20. With the large vertical opening angle of approximately 40◦_{, the directional antenna can receive signals without much}

attenuation over a large span of distances when mounted with a vertical angle between 39◦_.

The directional antennas performance of an increasing distance between the UAV and jammer during a flight can be seen in Figure 35. The UAV kept a fix altitude between the different distances. The altitude was kept at approximate 13 meters above ground instead of 20 meters, due to weather conditions during the flight.

Figure 35: This graph is based on RSSI data from the second UAV flight when the UAV performed rotations at different locations with the directional antenna mounted. The graph plots the RSSI maximum values from the different rotations at a certain distance and is compared to a quadratic plot based on Friis formula. The maximum power peaks are obtained when the directional antenna is aimed directly at the jammer. LOS during the entire test phase.

Figure 35 reveals that the RSSI measurement of the LOS signal aligns very well with the quadratic curve which is related to Friis equation. The reason the RSSI decreases as it does

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with increased distance is due to the free-space path loss model [25]. Since the RSSI decreases depending on the distance and the current directional antenna, the antenna should be considered to be replaced by an even higher gain directional antenna if RSSI measurements are to be performed at even larger distances. However, a higher gain comes at a cost of a smaller beamwidth of the main lobe.

9.2 GPS accuracy and reliability

To increase the update rate of GPS coordinates, a custom application was written using the libgps library in the C programming language [26]. This software update was added to the MikTran platform and was tested with the UAV and GPS antenna. The route of the UAV was performed on foot at FOIs courtyard, see Figure 36. The GPS achieved a Position Dilution of Precision (PDOP) of 1.4 and had an accuracy of within one meter when compared to satellite imagery of the area. More importantly, as seen in the figure the GPS secures an accurate fix within seconds of heading outside the building and tracks the walked trail accurately.

Figure 36: Test with the GPS logging at FOI’s courtyard. The movement of the GPS is based on the taken course on foot and are marked as white dots on the map. The dots are very accurate and the satellite imagery are acquired from Eniros website [11].

9.3 Performance of localization methods

This sub-section will cover the performance of the different methods of localization methods. 9.3.1 LS algorithm using dipole antenna

After the second flight was performed and RSSI data examined, it proved to reveal two maximum peaks since the transmitter is a dipole antenna with the radiation pattern as in Figure8a. Since the receiving antenna also is a dipole antenna, the received power right above the signal jammer is significantly lower. See Figure37for a view at the two peaks.

Due to this phenomenon the algorithm that’s being used will point out one of the two peaks as a transmitting source, which is not especially accurate since the jammer is located at the valley between the two maxima. This resulted in a margin of error of at least thirty meters from the correct GPS-spot, and the UAV was in presence of the jammer by at least one hundred meters.

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(a) (b)

Figure 37: Surface plot of the radiated power over the area covered by the UAV’s flight. The two peaks on either side of the jammer due to the radiation pattern are clearly visible. The X-axis and Y-axis represent normalized coordinates, the Z-axis shows the RSSI. (a) Shows a perspective view, (b) shows a top view.

9.3.2 Triangulation method in the courtyard

In order to improve the RSSI directon estimate at the different rotations, the -4 dB span of the RSSI is used. This span is useful since the maximum RSSI may not be the correct heading of the signal source, and the median angle of the -4 dB region is more accurate to the correct bearing. See Figure 38for the 3-4 dB span related to the ground test on the courtyard of the directional antenna.

Figure 38: Measured RSSI from the triangulation test in the courtyard. The two plots show the results from the rotation of the UAV at the a) and b) point in Figure39, respectively.

The courtyard test with the hand held UAV was performed at five different locations, see Figure

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Figure 39: The results from the triangulation test in the courtyard. Almost all of the five triangle headings are directed at the estimated direction of the jammer based on the -4 dB boundary with a ±5◦ _{spread. The white cross shows the jammers true location. a) and b) represents two different}

rotation locations and can be referred to Figure38a) and b). 9.3.3 Triangulation method during flight

The same triangulation principle as in section 9.3.2 was used with the UAV airborne. Wind made the UAV unstable during some of the rotations, causing noisy measurements. With the filtering method described in section 3.2.2, most of these measurements were removed while the more accurate measurements remained. Three of the four final direction estimates cross each other at a point approximately two meters from the true position of the jammer. See Figure40b for the estimated jammer directions of the rotations.

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(a) (b)

Figure 40: Triangulation results from the third flight with each direction estimate drawn as a 2◦

wide cone. The cross marks the true location of the jammer. Due to strong winds several of the measurements were inaccurate. Using the filtering method described in section 3.2.2 the seven measurements shown in (a) were reduced to the four shown in (b).

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10 Conclusion

In this chapter the conclusions based on the results in Chapter 9 is presented.

10.1 Localization based on real flights

Two different methods were evaluated, each with its own antenna, flight strategy and technique. The idea of the first method is that it could provide an exact position if the general area of the jammer is known. This method will make the UAV eventually pass over the jammer and receive one maximum RSSI peak per transmitting lobe it passes. These two peaks will be further apart the higher the altitude of the UAV and will cause the algorithms estimation to settle on the wrong location. Since the goal of this method was high-precision positioning in a smaller area it is not deemed suitable.

The RSSI maximum power peaks at different distances are presented in Figure35 and its al-most perfectly satisfactory with the quadratic decrements of RSSI from the theory predicted in Friis equation. This localization method that performs two rather quick position estimations is more efficient than the first method, which uses a dipole antenna. This is due to the fact that the first method searches a field with the wide zigzag pattern that is more time consuming than the second method consisting of the directional antenna. Both of these methods works to verify if there is in fact a transmitting signal jammer in an area of interest. When it comes to accuracy, the directional antenna is considerably better.

The both flights on the 7th of June verifies that the second method with triangulation in combi-nation with the median RSSI values with noisy rotation filtering gives a position estimation error that’s less than 3 meters.

The location of the true jammer location spot was only estimated once per flight using a smart-phone. The accuracy of this reference location could be improved by using several GPS devices or using a more accurate device. According to the satellite imagery, the real jamming location was placed a bit closer to the small truck road that’s visible in Figure40, making the position estimate of the third flight even more accurate.

10.2 Reliability of the user interface

The performance of the developed simulation environment GUI gives a simplified estimation on what to expect from a real UAV flight with RSSI data. Since it does not take landscape reflections from objects such as mountains, trees or water into account, it differs from measurements in the real world. It is not very usable for testing the triangulation method, since it will yield a perfect result every time. In other words, the GUI is more suitable for simulating the first method with the dipole antenna since the main problem with this method comes from the transmitting antennas radiation pattern which it does take into account.

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10.3 Goals

There are three goals regarding this thesis that has been evaluated: • Performance investigation of antennas and considerations.

This goal has been accomplished. The directional antenna RSSI proved to be suitable for the purpose and wasn’t affected by any reflections from the UAVs platform and maintained a high directional accuracy, compared to that of the dipole antenna which proved unreliable. • Development of a simulation environment to test position estimation methods.

This goal has been accomplished. The developed GUI was implemented with similar radiation pattern characteristics and yielded similar results with peaks in a circle around the jammer and a valley right above the jammer.

• Evaluation and comparison of signal source positioning methods.

This goal has been accomplished. The two methods, the LS algorithm method using the dipole antenna and the triangulation method using the directional antenna were implemented and tested.

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11 Future work

In this chapter some ideas of future improvements and developments regarding this master thesis area in how to locate a transmitting jammer with a UAV are presented.

11.1 Frequency-hopping implementation

The UAV antenna equipment for this thesis will only search for jammers at a CW with a fixed fre-quency. However there are more complicated jammers that work in frequency-hopping. Localizing these require broadening the received spectrum and may also require analysis of the signal.

11.2 UAV improvements

The UAV platform in this thesis is quite strongly affected by wind, mostly due to its size and weight. Using a bigger UAV will likely make measurements more stable and reliable in windier conditions. A landing gear will also make taking off and landing easier since without landing gears the directional antenna is the first point of contact with the ground.

A replacement of the directional antenna to an antenna with a suitable beamwidth is useful if the measurements are to be done at longer ranges than this thesis, which ranged from approximately 30-200 meters. As Figure 35 shows, the RSSI peak values decreases as the distance increases. There is no antenna that will evade this outcome, but a directional antenna with a higher gain than the panel antenna that was used will receive RSSI data at longer ranges before merging with the noise levels.

The sample rate of the UAV’s flight computer can also be increased, this will allow more samples while doing the triangulation rotations if the RSSI measurement sample rate is also increased.

Figure 41: This is the UAV flights that were performed on the 7th of June. The tight line cluster reveals the UAV rotations for the RSSI measurements and the alternating altitude along the z-axis shows that the UAV was affected by the winds.