Nghiên cứu giải pháp nâng cao khả năng chống nhiễu cho các bộ thu định vị GNSS tiên tiến robust signal processing techniques for modern GNSS receivers (tt)

A popular method for robust GNSS receiver performance is using multiple physical antenna elements which is so-called as an antenna array.. The existing antenna array frontend for GNSS re

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INTRODUCTION

With the development of new navigation system (Galileo-European system and BEIDOU-Chinese system), and the modernization of the existing navigation system such as GPS and GLONASS, the positioning performance of GNSS has been significantly improved GNSS services not only provide position but also provide high precise timescale for synchronizing systems such as telecommunication and network

Although they are widespread coverage of applications in many important sectors, the signals and services of GNSS systems are highly sensitive to malicious radio frequency interference (RFI) as well as jamming and spoofing; meanwhile, the quality of such services is not guaranteed to the conventional users Technically, the GNSS signal is transmitted from satellites away from Earth (about 20.000 km), so when it comes to receivers, the signal power is smaller than the background noise about

1024 times (26dB) [1] Therefore, any source of interference (jammer, digital terrestrial communication systems, ionosphere scintillation) may reduce the quality of the received signal, which in turn can disable the operation of the receiver In addition, because the GNSS systems are often under the management of military based organizations [2] [3] [4], the open services (e.g., GPS L1 C/A, Beidou B1, GLONASS L1OF) are provided to users without any guarantee of the reliability

Nowadays, ensuring reliable position and time information is essential in many applications ranging from transport applications to emergency applications Hence, the modern receivers must be able to detect the interference to determine the reliability of the position In addition, the position and time information must be available even where the GNSS signal is not continuous

A popular method for robust GNSS receiver performance is using multiple physical antenna elements which is so-called as an antenna array This technique has been studied in the 1940’s with the widely using in the radar and telecommunications applications [5] [6] [7] [8] It is considered

as a promising method in GNSS receivers where spoofing, jamming and interference are emerging threats Although there are several studies in using array-based processing for GNSS receivers [9] [10], there are

several existing issues involved to the implementation in a GNSS receivers Although using 2 bits in ADC is sufficient for GNSS receiver [1], it makes the GNSS receivers less robust to the threats Secondly, the number of antenna elements is also limited due to the bandwidth of interfaces The existing antenna array frontend for GNSS receivers pack all element samples into a single packet and send to digital processing chains through a single interface

A different method for robust GNSS receiver is the use of snapshot positioning-based receiver (coarse-time positioning) It is considered as

an efficient method that can be applied to the area where the continuous GNSS signal tracking is not guaranteed due to interference or jamming [11] [12] Recent studies have been improved its positioning performance

on the GPS L1 snapshot receiver [13] [14] [15] but the using constellation and INS integration in snapshot receiver have not been explored sufficiently in previous efforts

multi-Taking everything into account, the dissertation presents the robust signal processing techniques for modern GNSS receivers This thesis shows how the synchronization issue in antenna array can be addressed to expand the elements to unlimited number in theoretically The technique

is also validated with both simulation and real data Also, the dissertation presents a complete solution from hardware to software of a multi-GNSS snapshot receiver which can achieve a similar performance with a traditional receiver while using few milliseconds of data Also, through the dissertation, all the simulations are conducted with the generated from

a software-based GNSS simulator The design and implementation of this simulator is introduced in this thesis

This thesis results have been published 6 conferences and 5 journals as listed in the attachment The works have been carried on Hanoi University

of Science and Technology (Vietnam) and Politecnico di Torino (Italy)

Thesis outline

The thesis is organized in 4 chapters as follows:

Chapter 1 – Fundamental Background: In this chapter, the background knowledge related to the stages of GNSS receiver architecture including: acquisition, tracking and data demodulation, and position computation are

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Chapter 2 - GNSS Signal Simulator Design and Implementation: In this chapter, the design, implementation of a GNSS software-based simulator are carefully considered As one of the most important parameter related

to the speed of signal generation, the effect of sampling frequency is also generalized theoretically in both simulator and receiver sides

Chapter 3 – Antenna Array Signal Processing in GNSS Receivers: This chapter focus on the solution enabling extending the number of elements and the quantization bit It is applied in a low-cost antenna array for detecting the source of spoofing and interference

Chapter 4 – Snapshot Signal Processing in GNSS Receivers: This chapter shows how the multi-constellation snapshot technique can be effectively implemented In addition, to improve positioning performance, the snapshot GNSS/INS integration is proposed

1 FUNDAMENTAL

1.1 GNSS positioning principle

This section will explain the general principle of GNSS navigation Basically, GNSS positioning is based on trilateration techniques In this technique, the receiver firstly determines the distance from its position to

at least 3 known points After that, the receiver’s position is determined

by the intersection of 3 sphere

Let’s us denote 𝐮 = [𝑥𝑢 𝑦𝑢 𝑧𝑢] and 𝐱𝑖 = [𝑥𝑖 𝑦𝑖 𝑧𝑖] being the position of the receiver and the satellite i The geometry distance from the receiver to satellite is defined as 𝑟𝑖 = ||𝐮 − 𝐱𝑖|| Clearly, the vector 𝐮 can be determined if we know the satellite position 𝐱 and the distance 𝑟

In GNSS receivers, the distance cannot be measured directly but it uses the transmission time from satellite to receiver Unfortunately, the receiver clock is not synchronized with the atomic onboard of GNSS satellites As a result, we have one more unknown variable

𝛿𝑡𝑢 besides 3 unknown elements of 𝒖

1.2 History and development of GNSS

1.3 GNSS Threats

1.4 GNSS Receiver Architecture

1.4.1 Signal Conditioning and Sampling

The architecture of the signal conditioning and sampling is illustrated as

in the corresponding figure

In this stage, the received signal is to condition to meet the requirement

of sampling process For simplify, we consider the GPS L1 signal from a satellite:

𝑠(𝑡) = √2𝑃𝑠𝐶(𝑡 − 𝜏)𝐷(𝑡 − 𝜏)cos(2𝜋𝑓𝑠𝑡 + 𝛷) (1)

where 𝑃𝑠 is the received power of the GPS L1 signal 𝐶(𝑡) and 𝐷(𝑡) denotes the code and data of the consdired satellite

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After the mixer, the received signal is separated into I and Q

component Without loss of generality, from now on, we will use the complex signal to represent the signal on I and Q channel

1.4.2 Acquisition

The acquisition stage is aimed to roughly estimate the code phase and Doppler shift of visible GNSS satellites In fact, the stage performs correlation with every Doppler frequency and code phase bin in the search space A satellite is considered as visible if there is the value of a cell in the search space higher than a specified threshold The code and frequency corresponding to the cell is the output of the acquisition The selected threshold must be considered carefully because it is related to the number of satellite in use that is proportional to the accuracy of the solution

1.4.3 Tracking and Data Demodulation

After the acquisition, the receiver has roughly code phase and Doppler frequency of every satellite in view However, those parameters are changing over time due to the change of the relative position between the satellite and receiver The tracking stage is aimed to keep align the replica local code and carrier and the received signal with the Delay Lock Loop (DLL) and Phase Lock Loop (PLL)

1.4.4 Positioning Computation

With the assumption that the received signal is acquired and tracked successfully from minimum 4 satellites in view Before performing PVT computation, the transmission time must be estimated

1.5 Countermeasures to GNSS Threats

2 GNSS Signal Simulator Design and Implementation

Stemming from the need of a flexible simulator which is capable to simulate reliable emerging threats in GNSS fields (i.e jamming, spoofing, and interference) beside the properties of a conventional simulator, the chapter present the design and implementation of a software-based simulator In addition, the chapter generalize the effect of sampling frequency on the positioning performance to suggest the suitable sampling frequency for simulations

The modeling methodology of the developed simulator will be presented

in this chapter Moreover, some experiments conducted on both the software receiver and commercial receivers (e.g Ublox, Septentrio) will

be reported in the report, so validating the adopted models and the simulator performance The achieved results reported in this chapter show that the developed simulator can be considered as a low-cost solution to simulate not only single antenna signal but also antenna array signals The simulator has been used for reliable simulating spoofing and interference (e.g multipath) [18]

2.1 Modeling methodology

2.2 Overview of the modeling of antenna array signals in GNSS receivers

2.2.1 General model of the received signal in GNSS receivers

Figure 2.1: The model of the received signal for a single antenna

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The received signal at the 𝑚th element can be considered as the combination of the line-of-sight (LOS) signals, multipath signals, ambient noise and interferences (intentional or unintentional) (Figure 2.1) It can be expressed as

Figure 2.2: GPS multi-antenna frontend

The developed simulator is able to generate GNSS signals along with the operations of the multi-antenna frontend Therefore, the input of the simulator contains the user trajectory, the navigation files, the filter characteristics, and the profiles of signal power, multipath, and interference The output of the simulator is the digitalized signals at each element of the antenna array The flowchart of the simulator’s operation is shown in Figure 2.3

Figure 2.3: Flowchart of the simulator

As illustrated in Figure 2.3, the simulator contains three main

processing blocks, namely: propagation delay computation,

navigation message encoding, and digitalized signal generation The first block computes the propagation delay between the visible

satellites and the receiver, and the ionospheric and tropospheric delays The second block encodes the navigation messages The last block synthesizes the given information data and generate the LOS and NLOS signals, interference, and noise

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2.3 Effect of sampling frequency on the performance of GNSS Receiver

2.4 Performance verification

2.4.1 Verification of the simulated antenna array signals

The performance of the simulator has been tested by applying the generated signal to an antenna array with four elements, as shown in Figure 2.4 To facilitate the test, the XYZ coordinates are chosen to coincide with the ENU coordinates The origin of the reference frame is located at the center of the first element, and the position of the four elements is indicated in Table 2.1

Table 2.1: The coordinate of 4 elements

Two stages of the receiver have been analyzed, namely: the tracking system and the PVT computation module In the first stage, by using the post-correlation tracking loop proposed by De Lorenzo in [20] for array signal processing, the differences in carrier phase between signals can be measured In the PVT computation stage, thanks to the use of an RTK algorithm, the position of the array elements can be discriminated at centimeters level of the element spacing

Figure 2.4: Antenna array configuration

In the first epoch of the simulation, six satellites have been utilized with the following configuration:

Finally, the logged data is fed to a well-known RTK tool named RTKLIB to compute PVT.The obtained result of the experiment conducted is plotted in Figure 2.6

Figure 2.5: Estimated position of elements (East-North)

Clearly, the accuracy of the achieved results relying on RTK algorithm is sufficient to determine the four element positions The

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achieved result confirms the capacity of the simulator to generate antenna array signals

Figure 2.6: Estimated position of elements (Up)

2.4.2 Antenna distortion simulation

In ideal condition, the antenna radiation pattern is assumed isotropic

In the simulator it is possible to define a region where degradations

of the antenna gain are present The geometry of the degraded region

is given in terms of azimuth and elevation, and the degradation is expressed as attenuation For example, the situation shows that the elements 1, 2, 3 and 4 are distorted with 0 dB, -4 dB, -6 dB, and -8

dB, respectively in the region:

𝑅 = {30 deg ≤ 𝐴𝑧 ≤ 60 deg

45 deg ≤ 𝐸𝑙 ≤ 75 degDuring the simulation experiment, the signal from the satellite PRN 1 will impinge the antenna in the perturbed region two minutes after starting

By observing the signal to noise ratio (SNR) of the PRN 1 in Figure 2.7, we can see that SNR decreases according to the degradation given in figure 11

Figure 2.7: The C/N0 of the satellite PRN 1

2.4.3 Verification of multipath simulation

2.5 Conclusion

In this chapter, we presented a modeling methodology for the

simulation of antenna array signals Also, several experiments were conducted to confirm the capability of the simulator to properly generate signals useful for different algorithms of array signal

processing

The predominant limitation of the present simulator is its low speed

in generating the signals In the future, this aspect will be improved

by using advanced programming techniques Besides, the simulator is

in progress to be able to include other constellations

In recent studies, there are several efforts to synchronize separate element

in antenna array such as [22] However, this technique cannot be applied

in GNSS receiver due to unique properties of GNSS signals

Basing on the proposed solution, this chapter also present an low-cost antenna array frontend for GNSS application In fact, the technique performs synchronization RTL2832 dongles obtained from Nooelec The operating frequency range of such dongles varies from

ultra-25 MHz to 1750 MHz covering the whole band of GNSS signals Moreover, the quantization bits of the ADC embedded in the frontend can expand to 16 bits Therefore, the proposed frontend is suitable for GNSS applications

A software is also developed for this frontend In addition to

collecting signals, this software synchronizes received signals among dongles and estimates frequency difference between elements Since each element of this frontend is a complete dongle with their own interface to the host computer, the signals from the elements are not received at the same time Moreover, regardless of the use of a

common clock for all elements, the tuned frequency of Local

Oscillator (LO) is different in each element Therefore, these issues must be addressed prior to the use of this frontend A full explanation

of the algorithm used in our software will be given in the next

sections