Multi-Layer Defences for Robust GNSS Timing Retrieval

Gioia, Ciro; Borio, Daniele

doi:10.3390/s21237787

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…In the first phase, the geometry of the system is verified. The geometry check is based on the Time Protection Level (TPL), computed as:

where

is the Weighted Approximated Time Protected (WATP), which contains the information on the measurements biases; while the

is the term representing the measurements noise [ 25 ].…”

Section: Timing Solutionmentioning

confidence: 99%

T-RAIM Approaches: Testing with Galileo Measurements

Gioia¹

2023

Sensors

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Several applications rely on time retrieved from Global Navigation Satellite System (GNSS), and this pushes for integrity tailored to timing. Integrity information could be broadcast by GNSS itself, but currently, there are no GNSSs providing such integrity information for a timing application. The integrity provided by GNSS itself could not be timely enough for real time users and does not include local effects due to multipath or other local interferences. In order to fill the gap, integrity can be locally/autonomously computed by the receiver using Timing Receiver Autonomous Integrity Monitoring (T-RAIM) algorithms. Three T-RAIM algorithms have been designed, implemented, and tested; specifically, the algorithms are Forward-Backward (FB), Danish, and Subset. The algorithms are applied to the classical Position Velocity and Timing (PVT) solution and to the time-only case assuming the receiver coordinates are known. Tests using two identical receivers located in different scenarios, open-sky and obstructed, have been carried out to validate the algorithms proposed. The increased redundancy obtained from the knowledge of the receiver coordinates play a fundamental role for the integrity algorithms performance. The benefits of the T-RAIM algorithms activation, in signal degraded conditions, clearly emerged in terms of frequency error and Allan Deviation (ADEV). A small increase of the execution time has been observed when the T-RAIM algorithms are used.

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“…In the first phase, the geometry of the system is verified. The geometry check is based on the Time Protection Level (TPL), computed as:

where

is the Weighted Approximated Time Protected (WATP), which contains the information on the measurements biases; while the

is the term representing the measurements noise [ 25 ].…”

Section: Timing Solutionmentioning

confidence: 99%

T-RAIM Approaches: Testing with Galileo Measurements

Gioia¹

2023

Sensors

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“…where ∆D is a range variation, which is proportional to the average Doppler frequency shift due to satellite motion along the receiver-satellite direction, and ∆g takes into account the changes in receiver-satellite geometry. Replacing expression (10) in (7) and with manipulation, the TDCP measurement equation is obtained:…”

Section: Carrier Phase-based Velocitymentioning

confidence: 99%

“…For the integration of LiDAR and camera, synchronization is fundamental. Usually the time stamp is obtained from the Pulse Per Second (PPS) generated by the GNSS receiver; in order to obtain a reliable time, the GNSS receiver should use barriers against interference [ 10 ], and the relevance of the integrity algorithm for timing the GNSS receiver is shown in [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Galileo-Based Doppler Shifts and Time Difference Carrier Phase: A Static Case Demonstration

Gioia,

Angrisano,

Gaglione

2023

Sensors

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The European Commission is designing and implementing new regulations for vehicle navigation in different sectors. Commission Delegated Regulation 2017/79 defines the compatibility and performance of the 112-based eCall in-vehicle systems. The regulation has a large impact on road transportation because it requires that all cars and light duty vehicles must be equipped with eCall devices. For heavy duty vehicles, a set of new regulations has been developed, starting from EU Regulation No 165/2014, in which the concept of smart tachographs was introduced to enforce the EU legislation on professional drivers’ driving and resting times. In addition, intelligent speed assistance (ISA) devices increase the safety of road users. These new devices fully exploit the Global Navigation Satellite System (GNSS) to compute position velocity and time (PVT) information. In all these systems, the velocity of the vehicle plays a fundamental role; hence, a reliable and accurate velocity estimate is of utmost importance. In this work, two methods for velocity estimation using Galileo are presented and compared. The first exploits Doppler shift measurements, while the second uses time difference carrier phase (TDCP) measurements. The Doppler-based technique for velocity estimation is widely adopted in current devices, while the TDCP technique is emerging due to its promising high accuracy. The two methods are compared considering all the Galileo signals including E1, E5a, E5b, E5 Alt BOC and E6. The methods are compared in terms of velocity errors for both horizontal and vertical components using real static data. From the tests performed, it emerged that the TDCP has increased performance with respect to the Doppler-based solution. Among the Doppler-based solutions, the most accurate solution is the one obtained with the E5 Alt BOC signal.

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“…Scholars are presently delving into its methodological principles and performance analyses [5][6][7][8], availability and integrity risk assessment [9][10][11], GNSS satellite selection strategy [12], scenarios involving multiple constellations and faults [8,[13][14][15], cross-integration with other disciplines [16], and applications in aviation, Precise Point Positioning (PPP), Real-Time Kinematics (RTK), and other fields [17][18][19][20][21][22]. In response to the integrity monitoring requirements of timing receivers with precisely known, stationary antenna coordinates, a Timing-Receiver Autonomous Integrity Monitoring (T-RAIM) algorithm has been proposed [23][24][25]. In order to meet the integrity monitoring needs of the aviation LPV-200 operation, an advanced receiver autonomous integrity monitoring (ARAIM) algorithm has been developed on the basis of the RAIM algorithm, and its performance is evaluated [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency Signal Integrity Monitoring Algorithm Based on Temperature Compensation Frequency Bias Combination Model

Guo,

Li,

Gong

et al. 2024

Remote Sensing

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To ensure the long-term stable and uninterrupted service of satellite navigation systems, the robustness and reliability of time–frequency systems are crucial. Integrity monitoring is an effective method to enhance the robustness and reliability of time–frequency systems. Time–frequency signals are fundamental for integrity monitoring, with their time differences and frequency biases serving as essential indicators. These indicators are influenced by the inherent characteristics of the time–frequency signals, as well as the links and equipment they traverse. Meanwhile, existing research primarily focuses on only monitoring the integrity of the time–frequency signals’ output by the atomic clock group, neglecting the integrity monitoring of the time–frequency signals generated and distributed by the time–frequency signal generation and distribution subsystem. This paper introduces a time–frequency signal integrity monitoring algorithm based on the temperature compensation frequency bias combination model. By analyzing the characteristics of time difference measurements, constructing the temperature compensation frequency bias combination model, and extracting and monitoring noise and frequency bias features from the time difference measurements, the algorithm achieves comprehensive time–frequency signal integrity monitoring. Experimental results demonstrate that the algorithm can effectively detect, identify, and alert users to time–frequency signal faults. Additionally, the model and the integrity monitoring parameters developed in this paper exhibit high adaptability, making them directly applicable to the integrity monitoring of time–frequency signals across various links. Compared with traditional monitoring algorithms, the algorithm proposed in this paper greatly improves the effectiveness, adaptability, and real-time performance of time–frequency signal integrity monitoring.

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Multi-Layer Defences for Robust GNSS Timing Retrieval

Cited by 5 publications

References 15 publications

T-RAIM Approaches: Testing with Galileo Measurements

T-RAIM Approaches: Testing with Galileo Measurements

Galileo-Based Doppler Shifts and Time Difference Carrier Phase: A Static Case Demonstration

Time–Frequency Signal Integrity Monitoring Algorithm Based on Temperature Compensation Frequency Bias Combination Model

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