P-RANSAC: An Integrity Monitoring Approach for GNSS Signal Degraded Scenario

Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an accurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work properly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers. This work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision tasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on subsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this work, a modification to the original RANSAC method is proposed and an analysis of its performance is conducted, processing data collected in a static test

A Galileo IOV Assessment: Measurement and Position Domain

The European GNSS, Galileo, is currently in its In-Orbit Validation (IOV) phase where four satellites are finally available for computing the user position. In this phase, the analysis of the measurements and Position Velocity and Time (PVT) obtained from the IOV satellites can provide insight on the potentialities of the Galileo system. A methodology is suggested for the analysis of the Galileo IOV pseudorange and pseudorange rates collected from the E1 and E5 frequencies. Several days of data were collected and processed to determine figures of merits such as RMS and maximum errors of the Galileo observables. From the analysis, it emerges that Galileo is able to achieve better accuracy with respect to GPS. A thorough analysis of the PVT performance is also achieved using broadcast ephemerides. Galileo and GPS PVTs are compared under similar geometry conditions showing the potential of the Galileo system.JRC.G.5-Security technology assessmen

Identification of Walker Identity Using Smartphone Sensors: An Experiment Using Ensemble Learning

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DNA Damage, Homology-Directed Repair, and DNA Methylation

To explore the link between DNA damage and gene silencing, we induced a DNA double-strand break in the genome of Hela or mouse embryonic stem (ES) cells using I-SceI restriction endonuclease. The I-SceI site lies within one copy of two inactivated tandem repeated green fluorescent protein (GFP) genes (DR-GFP). A total of 2%–4% of the cells generated a functional GFP by homology-directed repair (HR) and gene conversion. However, ~50% of these recombinants expressed GFP poorly. Silencing was rapid and associated with HR and DNA methylation of the recombinant gene, since it was prevented in Hela cells by 5-aza-2′-deoxycytidine. ES cells deficient in DNA methyl transferase 1 yielded as many recombinants as wild-type cells, but most of these recombinants expressed GFP robustly. Half of the HR DNA molecules were de novo methylated, principally downstream to the double-strand break, and half were undermethylated relative to the uncut DNA. Methylation of the repaired gene was independent of the methylation status of the converting template. The methylation pattern of recombinant molecules derived from pools of cells carrying DR-GFP at different loci, or from an individual clone carrying DR-GFP at a single locus, was comparable. ClustalW analysis of the sequenced GFP molecules in Hela and ES cells distinguished recombinant and nonrecombinant DNA solely on the basis of their methylation profile and indicated that HR superimposed novel methylation profiles on top of the old patterns. Chromatin immunoprecipitation and RNA analysis revealed that DNA methyl transferase 1 was bound specifically to HR GFP DNA and that methylation of the repaired segment contributed to the silencing of GFP expression. Taken together, our data support a mechanistic link between HR and DNA methylation and suggest that DNA methylation in eukaryotes marks homologous recombined segments

Smartphone GNSS Performance in an Urban Scenario with RAIM Application

In an urban scenario, GNSS performance is strongly influenced by gross errors in the measurements, usually related to multipath and non-line-of-sight phenomena. The use of RAIM algorithms is a common approach to solve this issue. A significant amount of the existing GNSS receivers is currently mounted on smart devices, above all, smartphones. A typical drawback of these devices is the unavailability of raw measurements, which does not allow fully exploiting the GNSS potential; in particular, this feature limits the use of RAIM algorithms. Since 2016, for few smart devices, it has been finally possible to access GNSS raw measurements, allowing the implementation of specific algorithms and enabling new services. The Xiaomi Mi 8 is equipped with the Broadcom BCM47755 receiver, able to provide dual-frequency raw measurements from quad-constellation GPS, Glonass, Galileo, BeiDou. In this work, the performance in an urban area of the Xiaomi Mi8 GNSS was analyzed. An important issue of smartphone GNSS is related to the antenna, which is not able to protect from the multipath phenomenon; this issue has a large probability to emerge in hostile environments like urban areas. As a term of comparison, the high-sensitivity receiver NVS NV08C-CSM, connected to a patch antenna, was used. In particular, the considered receivers were placed in the same location, and their positions were estimated in single point positioning, applying a classical RAIM algorithm. An error analysis was carried out, and the obtained results demonstrated the effectiveness of RAIM when applied to Xiaomi Mi8 GNSS measurements

A resampling strategy based on bootstrap to reduce the effect of large blunders in GPS absolute positioning

In the absence of obstacles, a GPS device is generally able to provide continuous and accurate estimates of position, while in urban scenarios buildings can generate multipath and echo-only phenomena that severely affect the continuity and the accuracy of the provided estimates. Receiver autonomous integrity monitoring (RAIM) techniques are able to reduce the negative consequences of large blunders in urban scenarios, but require both a good redundancy and a low contamination to be effective. In this paper a resampling strategy based on bootstrap is proposed as an alternative to RAIM, in order to estimate accurately position in case of low redundancy and multiple blunders: starting with the pseudorange measurement model, at each epoch the available measurements are bootstrapped---that is random sampled with replacement---and the generated a posteriori empirical distribution is exploited to derive the final position. Compared to standard bootstrap, in this paper the sampling probabilities are not uniform, but vary according to an indicator of the measurement quality. The proposed method has been compared with two different RAIM techniques on a data set collected in critical conditions, resulting in a clear improvement on all considered figures of merit

A comparison between resistant GNSS positioning techniques in harsh environment

Environments as urban areas are critical for GNSS, because several obstacles block, attenuate and distort the signals; consequently, frequent blunders are present among the measurements and their effect on the position could be harmful. Two approaches are usually adopted to tackle the blunder issue, RAIM and robust estimation, and both are effective in case of high redundancy and single blunders. An alternative method, based on bootstrapping, i.e. random sampling with replacement, the available measurements, has recently emerged. The performance of the considered methods could be augmented by exploiting suitable measurement error models, which are used to differently weighting the measurements in RAIM and robust estimators, and to defining not uniform sampling probabilities in bootstrap; several models, based on the most common measurement quality indicators, carrier-to-noise ratio and satellite elevation, are herein analyzed. In this work, the three techniques, coupled with several error models, are compared in terms of mean, RMS and maximum position errors, processing data from urban scenario. The results demonstrate the best performance of bootstrap method, which works effectively in case of multiple blunders and/or the lack of redundancy, when RAIM and robust techniques are often unsuccessful. Moreover, the results highlight the importance of a careful choice of a measurement error model

Real-Time Receiver Clock Jump Detection for Code Absolute Positioning with Kalman Filter

In global navigation satellite systems (GNSS) navigation the receiver and satellite clocks play a key role. The receivers are usually equipped with inaccurate quartz clocks, which experiment large drift relative to system time and consequently offset growing very fast; receiver manufactures bound the magnitude of the receiver clock offset to prevent it becomes too large and the actual bounding procedures vary from one manufacturer to another. The most common approach consists of introducing discrete jumps when the offset exceeds a threshold (usually 1 ms). This method is common in low-cost GNSS receivers and influences several applications as differential positioning, cycle-slip detection, precise point positioning technique, absolute positioning with Kalman filter. In this work some techniques to detect and account for millisecond clock jump, suitable for code positioning of a single receiver with Kalman filter, are proposed. Two deterministic algorithms to detect receiver clock jumps are shown: in measurement and parameter domain. The technique in measurement domain uses current pseudorange measurements compared with pseudorange and Doppler measurements at previous epoch; the technique in parameter domain compares current and previous least squares estimations of receiver clock bias, considering the clock drift. Two different approaches are described to account for the clock jumps, once detected, a deterministic one, consisting of fixing the pseudorange discontinuities, and a statistic one, consisting of suitably varying the Kalman filter settings. A static GNSS data set is processed with and without the proposed algorithms to demonstrate their efficiency

Robust Kalman Filter applied to GNSS positioning in harsh environment

GNSS-based navigation, nowadays the most spread system, is very challenging in scenario where the presence of obstacles could block, diffract or reflect the signals coming from satellites. In this harsh environment the navigation algorithm has to face out with low redundancy of measurements or the presence of blunders among them. A possible approach to overcome this problem is the use of robust estimation that is applied, in this study, to single point positioning, implemented with an Extended Kalman Filter (EKF). In the update phase of the filter, the measurement covariance matrix is computed considering the robust Huber method. The satisfactory performance of the proposed method are investigated processing real data, collected by a low cost GNSS receiver in an urban scenario