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

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 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 multiconstellation and INS integration in snapshot receiver have not been explored sufficiently in previous efforts 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 conferences and 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 chapters as follows: Chapter – 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 revised Chapter - 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 – 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 – 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 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 known points After that, the receiver’s position is determined by the intersection of sphere Let’s us denote 𝐮 = [𝑥𝑢 𝑦𝑢 𝑧𝑢 ] and 𝐱 𝑖 = [𝑥 𝑖 𝑦 𝑖 𝑧 𝑖 ] being the position of the receiver and the satellite i The geometry distance from the receiver to satellite is defined as 𝑟 𝑖 = ||𝐮 − 𝐱 𝑖 || 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 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: (1) 𝑠(𝑡) = √2𝑃𝑠 𝐶(𝑡 − 𝜏)𝐷(𝑡 − 𝜏)cos(2𝜋𝑓𝑠 𝑡 + 𝛷) 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 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 satellites in view Before performing PVT computation, the transmission time must be estimated 1.5 Countermeasures to GNSS Threats 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 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 𝑁 𝑚 (𝑡) 𝑅𝐿1 = 𝑚 ∑ 𝑆𝐿1,𝑘 (𝑡) 𝑘=1 𝑀 𝐾 𝑚 + ∑ 𝑆𝑀𝑃,𝑘 (𝑡) 𝑘=1 +𝜂 𝑚 (𝑡) + ∑ 𝐼𝑘𝑚 (𝑡) (2) 𝑘=1 Note that, as shown in Figure 2.2, the local oscillators are shared among the channels in order to synchronize them 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 2.2.2 Interference 2.2.3 Multipath 2.2.4 Noise Although the noise may arise from various sources, it mainly depends on the front-end circuitry It is generally modeled as white Gaussian In the case of an array, each front-end introduces an independent white Gaussian noise 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 Element X (m) Y (m) Z (m) 0 -0.094 0 -0.094 -0.094 -0.094 Table 2.1: The coordinate of 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 10 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, and are distorted with dB, -4 dB, -6 dB, and -8 dB, respectively in the region: 30 deg ≤ 𝐴𝑧 ≤ 60 deg 𝑅={ 45 deg ≤ 𝐸𝑙 ≤ 75 deg During the simulation experiment, the signal from the satellite PRN will impinge the antenna in the perturbed region two minutes after starting By observing the signal to noise ratio (SNR) of the PRN in Figure 2.7, we can see that SNR decreases according to the degradation given in figure 11 11 Figure 2.7: The C/N0 of the satellite PRN 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 12 Antenna array processing for GNSS Receivers 3.1 Introduction This chapter presents a solution to extend the number of elements in antenna array frontend for GNSS receivers In this solution, the signal from elements is not necessary to synchronize right after ADC but they are done by post-processing technique With this solution, the antenna array element is relaxing the dependence of the interface bandwidth Therefore, the antenna array frontend has advantages such as many quantization bits, compactness, and scalability 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 ultralow-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 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 13 3.2 The proposed solution for synchronizing separated antenna array element (A) Traditional Architecture (B) Proposed Architecture Figure 3.1: The architecture of antenna array based GNSS receiver Without loss of generality, we consider an antenna array with elements We can assume that the received signal at the first element as follows: 𝑠0 (𝑛𝑇𝑠 ) = √2𝑃𝑠 𝐶(𝑛𝑇𝑠 − τ0 )𝐷(𝑛𝑇𝑠 − 𝜏0 )exp(𝑗2𝜋𝑓𝑑 𝑛𝑇𝑠 (3) + Φ0 ) where 𝑃𝑠 is the power of the received signal 𝐶( ) is the CA code of the GPS signal 𝐷( ) is the data of the GPS signal 𝜏0 is the code delay 𝑓𝑑 is the remain frequency after down converting to baseband 𝛷0 is the carrier phase of the received signal The corresponding signal on the second element: 𝑠1 (𝑛𝑇𝑠 ) = √2𝑃𝑠 𝐶(𝑛𝑇𝑠 − τ0 − 𝑚𝑇𝑠 )𝐷(𝑛𝑇𝑠 − 𝜏0 (4) − 𝑚𝑇𝑠 )exp(𝑗2𝜋(𝑓𝐼𝐹 + Δ𝑓)(𝑛𝑇𝑠 − 𝑚𝑇𝑠 ) + Φ0 + ΔΦ) where 𝑚𝑇𝑠 is the time difference between elements due to the receiving 14 process, ΔΦ is the time difference caused by antenna positions To model antenna array signals, we assume that a far-field signal impinges an antenna in the direction expressed by the azimuth and elevation angles (𝜙, 𝜃) 3.2.1 Determining the samples difference 3.2.2 Determining the clock phase shift 3.3 Implementation a low-cost antenna array According to [23] [24], the combination of RTL2832U chipset and R802T2 turner was proved to satisfy the requirements of a GPS frontend The dongles are combined to make a low-cost antenna array The key of antenna aray frontend design is the use of common clock for both oscillator and ADC clock Therefore, to adapt the turner to the antenna array application, the default crystal oscillator equipped on all dongles are removed A TCXO is then connected to all dongles Before using the antenna array, we applied the proposed solution to tackle the two problems: (A) how to synchronize data taking from the frontend using multiple USB interfaces, (B) how to determine the clock phase shift of every frontend The second issue resulted from the internal architecture of the turner nevertheless the use of a common clock for both frontends 3.4 Antenna array frontend verification We conducted experiments with our simulator and to verify: (A) phase difference between frontends (B) the 4.4 dB gain using beamforming algorithm (3 elements frontend) 3.4.1 Phase difference between frontends To verify the reliability of the antenna array frontend, we conducted the following experiment Firstly, the elements of the frontend are connected to a signal splitter We then transmitted the simulated signal to the frontend The simulated signal was generated using our simulator [3] Using simulator helps us 15 control the external factors (e.g multipath, interference) which can corrupt the received signal Because the signals of all simulated satellites are transmitted from the same source The phase difference between the elements now depends on the cable length and internal architecture of each element Clearly, this delay is the same for all satellite Therefore, the phase difference must be comparable to all satellites in view As expected, Figure 3.2 shows the consistency of the carrier phase of all satellites Figure 3.2: Tracking output of satellites in view 3.4.2 Carrier to noise ration improvement In idealistic condition, the gain of using antenna are supposed to be the same Therefore, a 3-elements can achieve 4.77dB in gain However, in realistic condition, the gain of a specific antenna differ from others To be specific, suppose that they are 𝑔1 , 𝑔2 , 𝑔3 , respectively, the gain of the beamed signal is as follows: 𝑔 = √𝑔12 + 𝑔22 + 𝑔32 16 (5) Figure 3.3: 𝑪/𝑵𝟎 of the satellite PRN 09 for the received signal at every element and beamed signal Figure 3.3 indicates that the carrier to noise ratio of the signals received by different elements are different However, the ratio of the beamed signal is much higher than that of element 3.5 Conclusion and discussion The chapter presented the practical consideration in designing an antenna array for GNSS application The result shown in this chapter is a very promising for not only GNSS application but also the other field In the future, we will use such antenna array frontend to suppress interference, point to the source of the interference and spoofing 17 GNSS Snapshot processing techniques for GNSS receivers Nowadays, GPS receivers are widely used in many applications ranging from vehicle navigation to unmanned vehicle guidance, from locationbased services to environment monitoring… The traditional architecture of GPS receiver has the signal processing part composed of stages: signal conditioning and digitization, signal synchronization (acquisition, and tracking), data demodulation, and position-time-velocity calculation [13] Among these stages, the most difficult one is the signal synchronization This stage is based on the correlation computation results between the received signal and its local replica to perform signal acquisition and tracking However, since the GPS satellites are located roughly 20,200 km from the Earth, the received signal is very weak even in open sky environment (nominal C/N0 value of 45dB-Hz) Therefore, the integration time for each correlation value must be long enough to achieve a reasonable processing gain so that the signal can appear from the noise floor (e.g nominal value being 1ms for coherent integration time) In harsh environment (under tree, indoor…), the longer coherent integration time is required In addition, for PVT computation, a standalone GPS receiver must be turned on for a minimum 30 seconds to download a full page of ephemeris data from at least satellites-in-view Even an assisted GPS solution, which basically requires a shorter time to first fix (TTFF), still needs seconds for decoding time stamps for Position-Velocity-Time (PVT) computation [13] These lead to the fact that a GPS positioning requires a huge computational resource, which also implies a huge power consumption In recent years, every smartphone has a GPS receiver on it, however, if the receiver stays on, the battery of the phone will be drained very fast Therefore, a more battery capacity is required, however, for devices which have big concerns on the size and weight (e.g smartwatches, kid tracker, pet tracker…), a low power consumption approach is needed for GPS positioning 18 Figure 4.1: Snapshot positioning architecture [14] introduces a technique, namely snapshot positioning In this technique, a user is equipped with a GPS data grabber, which collect GPS signal on site The dataset is then transmitted to a server (see Figure 4.1) At the server side, the available GPS data (provided by another GPS receiver) and the received dataset are used together to compute the position of the user In this technique, the most difficult tasks – signal synchronization and position computation – are performed at the server side, whereas on the user side, only a simple GPS data grabber with a communication modem is needed By this way, the computational requirement at the user side is relaxed, and eventually, the power consumption is reduced significantly Although snapshot receiver was first proposed by NASA [14] in 1997, it has been widely studied in recent years due to the increasing demands on low power consumption positioning for mobile devices, especially for smartwatch, and object trackers In [14], the requirement for using the technique is that we need to know an approximate position (so-called prior solution), which must be less than 150 km, equivalent to a half code-length, from the true position However, that information is not always available in reality To overcome that distance limitation, recent studies, which propose feasible designs of snapshot receivers for mobile computing [25] [26] use the position of the base stations of the cellular network as the prior solution However, due to the policy of telecommunication companies, that information of base stations is also not always provided The work in [3] uses the Doppler positioning method in order to provide the prior solution for the snapshot positioning Although the Doppler positioning is not so precise, however, that level of accuracy already satisfies the 150-km-requirement However, 19 the architecture in [15] requires the fine estimation of code delay Therefore, the tracking process is mandatory, this leads to power consumption due to the correlation computation Besides the signal processing part which is already relaxed by the snapshot technique, the communication part needs to control the power consumption also Therefore, the size of the dataset must be reduced as much as possible to meet that requirement In literature, the GPS data grabbers use bits for quantization, with the sampling frequency of 2.046 MHz The sampling frequency has an important impact to the accuracy of the positioning and cannot be reduced due to the Nyquist criteria Meanwhile, the number of quantization bits has impact to the sensitivity of the positioning, which can be compensated by extending the integration time In addition, in the view point of hardware design and implementation, the 1-bit data stream is much simpler and more stable than the 2-bit one since the Serial Peripheral Interface (SPI) interface, which is a fast data transfer protocol, can be used directly in 1-bit stream to facilitate the data transfer between the frontend and the microprocessor This chapter introduces a novel design of low power consumption GPS positioning solution based on snapshot technique In this design, a complete snapshot solution including GPS data grabber, and server program is presented The snapshot processing leverages a 1-bit quantization frontend and the Doppler positioning in order to achieve the low power consumption objective The solution is validated with real GPS signal The validation results show a 77% reduction in power consumption in comparison with a typical commercial GPS receiver, meanwhile the accuracy level is about 14 m in horizontal position, which can satisfy most of mobile applications The remaining part of the chapter is as follows Section Proposed Design4.1 presents the architecture of the grabber and the overview of snapshot technique 4.1 Proposed Design of GNSS Snapshot Receiver The proposed design contains parts, namely GPS grabber for collecting the IF digitalized data and a server software for post-processing to 20 estimate PVT of the GNSS grabber 4.1.1 GNSS Grabber 4.2 Server Software 4.3 Loosely coupled Snapshot GNSS/INS 4.4 RESULTS 4.4.1 Standalone Snapshot GNSS Receiver Firstly, we evaluate the positioning performance of our solution with the live-sky signal collected by our GPS grabber The data was collected on July, 19th, 2017 at HUST The configuration of this experiment is shown Due to the similar behaviors of other signals, we shows the acquisition result of a strong signal (PRN20) and a weak signal (PRN12) As observed, the peak is emerged from noise floor even this is the weak signal The results verify that the solution is able to use in harsh environments where the GPS signal power is low With 10 milliseconds of integration time, satellites in view are acquired The code phase, Doppler shift, and Peak to Average Power Ratio (PAPR) of acquired satellites are represented in the corresponding figure Since all measurements show similar behaviors, we take the first measurement to visualize the work of our proposed solution To compute the user position, the Doppler positioning is performed first to produce the initial solution As shown in the corresponding figure, the produced position (blue one) is about km from the user position This confirms that the position produced by the Doppler positioning meets the requirement of a-priori solution for the snapshot positioning (below 150 km from the user position) Using the output from the Doppler positioning, the solution of the snapshot algorithm is converged after iterations (red ones) The accuracy of the receiver is shown in the corresponding figure and Table Clearly, with 14 meters of accuracy, the solution approaches the accuracy of commercial receivers 21 Table Positioning Performance of the proposed solution (100 measurements with the fixed antenna) 𝛿𝐸 (m) 14.12 𝛿𝑁 (m) 14.66 𝛿𝑈 (m) 40.7 𝛿(m) 45.58 Clearly, with 45.58 m of the standard deviation, the accuracy of our design is better than the previous work (62.81 m) [25] 4.4.2 Snapshot GNSS/INS Integration In the second experiment, we benchmark the power consumption of our solution with Ublox – a low power consumption receiver on the market In this experiment, we measure the average current of our grabber with the various time periods between signal collections The average power of our design is inversely propositional to the measurement period while that of U-blox is unchanged with 22mA in the average chipset This is because the grabber lasts 180 milliseconds in one period to collect the signal nevertheless the update period time and enter the backup mode for a remaining time In our experiments, the chosen IMU sensor and GPS receiver were 3DMGX3 provided by MicroTrain and LEA-6P provided by Ublox, respectively (Fig 6) The conducted experiments with two scenarios are described as below In our experiment, the GPS receiver and IMU are all mounted to a fixed frame and placed on the vehicle The vehicle is then moved around the HUST campus following the trajectory illustrated in Fig In this mode, the position of every point in the reference trajectory is estimated using RTK technique The base station is placed at a reference point in HUST We verify the performance of the proposed model in both low DOP and high DOP cases Therefore, we decide to choose satellites following predefined scenarios as follows In the first case, the chosen satellites to compute PVT must satisfy the low DOP (Dilution Of Precision) value criteria (Fig 8) The experiment lasts 250 seconds In the second case, the setup is the same as in the first case except for the chosen satellites for calculating PVT solution In this case, the chosen satellites satisfy the high DOP value criteria (Fig 10) 22 Conclusion In this chapter, related theory and implementation of a new design for energy effective positioning have been presented The results verify that the new design can reduce the size of dataset while the overall performance is higher than previous studies Moreover, the proposal can give the position without a-prior knowledge of the initial position Future works will focus on improving the accuracy of the solution in different scenarios 23 CONCLUSIONS AND FUTURE WORKS The content of this thesis aims to investigate the potentials and challenges of modern GNSS receivers under threats Through the investigation of properties of modern GNSS receivers, some improvement its performance is presented In this thesis, the works devoted to the improve the modern GNSS receivers are the main contributions, which can be summarized as follows: Design and implementation of a software-based GNSS simulator (Chapter 2): A complete theory and implementation of a GNSS simulator which is capable of simulating antenna array signals The block diagrams, theoretical and practical analyses of all stages in the simulator are provided especially sampling frequency The performance evaluation results prove that the generated signals are reliable to the live sky signals Testing with multiple frontends will be the future works of this section Antenna array processing for GNSS Receivers (Chapter 3): A technique for extending the antenna element to infinite theoretically is proposed for the first time in this thesis The technique is proved suitable for low-cost antenna array frontends Robust GNSS Snapshot Receiver (Chapter 4): The multi-GNSS snapshot receiver is proposed Such receiver is proved that it is suitable for the discontinuous GNSS signal due to jamming, spoofing or interference 24 ... explored sufficiently in previous efforts Taking everything into account, the dissertation presents the robust signal processing techniques for modern GNSS receivers This thesis shows how the... 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 The received signal at the... array processing for GNSS Receivers 3.1 Introduction This chapter presents a solution to extend the number of elements in antenna array frontend for GNSS receivers In this solution, the signal