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Journal articles on the topic 'RAIM'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Jiang, Yiping, and Jinling Wang. "A-RAIM and R-RAIM Performance using the Classic and MHSS Methods." Journal of Navigation 67, no. 1 (August 15, 2013): 49–61. http://dx.doi.org/10.1017/s0373463313000507.

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Two Receiver Autonomous Integrity Monitoring (RAIM) architectures, Advanced RAIM (A-RAIM) and Relative RAIM (R-RAIM), are compared with two different RAIM algorithms, the Classic method and the Multiple Hypothesis Solution Separation (MHSS) method. The difference between A-RAIM and R-RAIM is in the positioning methods that produce different error models and projection matrices for integrity monitoring. The difference between RAIM algorithms lies in the methods of risk distribution. The influences of different positioning methods on integrity results are analysed in this paper via a generalized RAIM framework. Simulation results for the LPV-200 service with worldwide coverage show that the R-RAIM position domain method has the best results, while the differences between these methods decrease with application of the optimization method.

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2

Santos, Juliana Pereira dos, Martamaria de Souza Ferraz Ribeiro, Maria Teresita Bendicho, Geraldo Bezerra da Silva Júnior, and Rosa Malena Fagundes Xavier. "Reações Adversas Imunomediadas em pacientes tratados com Inibidores de Checkpoints Imunológicos em um Hospital filantrópico de Salvador." Research, Society and Development 10, no. 2 (February 28, 2021): e58510212928. http://dx.doi.org/10.33448/rsd-v10i2.12928.

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Introdução: Os Inibidores de Checkpoints Imunológicos (ICI) bloqueiam os efeitos inibitórios em receptores como o CTLA-4, PD-1 e PD-L1 e reestabelecem a imunidade antitumoral. Dessa maneira, estão associados a Reações adversas Imunomediadas (RAim) gastrintestinais, dermatológicas, hepáticas e endócrinas. Objetivo: Analisar o perfil das reações adversas imunomediadas em pacientes oncológicos tratados com ICI em um hospital de Salvador/Ba. Método: Trata-se de um estudo observacional, transversal, retrospectivo. Foram avaliadas todas as notificações de reações adversas associadas ao tratamento oncológico com os ICI, documentadas no período de janeiro de 2015 a maio de 2020. A pesquisa foi aprovada pelo Comitê de Ética em Pesquisa, conforme o nº do parecer 4.074.757/2020. Resultados: Durante o período do estudo, 27 pacientes fizeram uso de ICI. Quanto as RAim, 17 desenvolveram RAim. Das RAim mais frequentes, 62% envolveram o Nivolumabe e 21% o Pembrolizumabe. 26% das RAim foram reações gastrintestinais, seguido de 19% reações diversas (como sangramento vaginal e neuropatia); 53% das RAim foram classificadas como reações moderadas. Dentre as medidas terapêuticas adotadas para resolução das RAim, 73% dos ICI foram descontinuados e substituídos por outros quimioterápicos; 53% dos pacientes utilizaram terapias de suporte para manejo de sintomas provocados pelas Raim. Quanto ao desfecho clínico, 53% pacientes se recuperaram das RAim, 24% se recuperaram com sequela e 23% foram a óbito. Conclusão: A introdução de terapêutica dirigida, acompanhamento farmacoterapêutico adequado e o monitoramento pela farmacovigilância favorecerão a identificação de reações adversas imunomediadas, e o uso seguro dos ICI.

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3

Radišić, Tomislav, Doris Novak, and Tino Bucak. "The Effect of Terrain Mask on RAIM Availability." Journal of Navigation 63, no. 1 (December 1, 2009): 105–17. http://dx.doi.org/10.1017/s0373463309990294.

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Receiver Autonomous Integrity Monitoring (RAIM) is a method, used by an aircraft's receiver, for detecting and isolating faulty satellites of the Global Navigation Satellite System (GNSS). In order for a receiver to be able to detect and isolate a faulty satellite using a RAIM algorithm, a couple of conditions must be met: a minimum number of satellites, and an adequate satellite geometry. Due to the highly predictable orbits of the GPS satellites, a RAIM availability prediction can be done easily. A number of RAIM methods exist; however, none of them takes into account the precise terrain masking of the satellites for the specific location. They consider a uniform fixed mask angle over the whole horizon. This paper will introduce the variable mask RAIM algorithm in order to show to what extent the terrain can affect the RAIM availability and how much it differs from the conventional algorithms.

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4

Sun, Jun, and Yong Gang Yang. "Analysis and Simulation of an Autonomous Integrity Monitoring Algorithm for Dual-Mode Navigation Receiver." Advanced Materials Research 765-767 (September 2013): 2097–100. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2097.

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A dual-mode receiver combination of GPS and Compass (BD) is introduced on the assumption when only one of the satellites had a failure, how to implement a weighting RAIM monitoring. And the GPS and BD in single mode and the GPS/BD dual-mode were simulated to produce the Fault Detection Rates and RAIM integrity availability. It is demonstrated that the dual-mode RAIM algorithm is superior to any kind of single-system RAIM algorithms.

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5

ANGUS, J. E. "RAIM with Multiple Faults." Navigation 53, no. 4 (December 2006): 249–57. http://dx.doi.org/10.1002/j.2161-4296.2006.tb00387.x.

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6

Wang, Zi Lu, and Bin Wu. "GNSS RAIM Performance Analysis for World Wide Area." Applied Mechanics and Materials 565 (June 2014): 217–22. http://dx.doi.org/10.4028/www.scientific.net/amm.565.217.

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Multi-constellations provide much better satellite geometrics, thus RAIM algorithms areexpected to achieve greater reliability and integrity performance. This paper mainly discusses different RAIM algorithms and gives simulations of RAIM availability and reliability of standalone GPS and integrated GPS/GLONASS, GPS/BEIDOU and GPS/GLONASS/BEIDOU constellations.The results show that multi-constellation improve RAIM availability and reliability greatly. It is no less than 99.7%for APV I. Also MDB values indicate thatinternal and external reliability of satellite navigation system can be enhanced by multi-constellation.

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7

Feng, Shaojun, Washington Y. Ochieng, David Walsh, and Rigas Ioannides. "A Highly Accurate and Computationally Efficient Method for Predicting RAIM Holes." Journal of Navigation 59, no. 1 (December 15, 2005): 105–17. http://dx.doi.org/10.1017/s037346330500353x.

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Receiver Autonomous Integrity Monitoring (RAIM) is a method implemented within the receiver to protect users against satellite navigation system failures. For a receiver to execute a RAIM calculation, two conditions must be met: a minimum number of satellites and adequate satellite geometry. The non-existence of the minimum number of satellites (five) is referred to as a RAIM hole. Current regional and global RAIM availability studies use spatial (grid-based) and temporal sampling intervals driven by a trade-off between accuracy and computation workload. The implication of minimising computational load is that accuracy is compromised and potential RAIM holes remain un-sampled, with potential risk to safety. This paper proposes a direct and computationally efficient method (as opposed to the grid-based search approach) to predict RAIM holes. The method is based on the precise computation of satellite coverage (footprint) boundaries, the intersection points and analysis of the topology of the regions of intersection. Test results show that the proposed method is highly accurate and requires minimal computational load compared to the current approach.

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8

Joerger, Mathieu, Stefan Stevanovic, Steven Langel, and Boris Pervan. "Integrity Risk Minimisation in RAIM Part 1: Optimal Detector Design." Journal of Navigation 69, no. 3 (January 6, 2016): 449–67. http://dx.doi.org/10.1017/s0373463315000983.

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This paper describes the first of a two-part research effort to find the optimal detector and estimator that minimise the integrity risk in Receiver Autonomous Integrity Monitoring (RAIM). In this first part, a new method is established to determine a piecewise linear approximation of the optimal detection region in parity space. The paper presents examples suggesting that the optimal detection boundary lays in between that obtained using chi-squared residual-based RAIM, and that provided by Solution Separation (SS) RAIM, as one varies the alert limit requirement. In addition, these examples indicate that for realistic navigation requirements, the SS RAIM method approaches the optimal detection region. The SS RAIM detection tests will be employed in the second part of this work, which focuses on the design of non-least-squares estimators to reduce the integrity risk in exchange for a slight increase in nominal positioning error.

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9

Kim, Daehee, and Jeongho Cho. "Improvement of Anomalous Behavior Detection of GNSS Signal Based on TDNN for Augmentation Systems." Sensors 18, no. 11 (November 6, 2018): 3800. http://dx.doi.org/10.3390/s18113800.

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The reliability of a navigation system is crucial for navigation purposes, especially in areas where stringent performance is required, such as civil aviation or intelligent transportation systems (ITSs). Therefore, integrity monitoring is an inseparable part of safety-critical navigation applications. The receiver autonomous integrity monitor (RAIM) has been used with the global navigation satellite system (GNSS) to provide integrity monitoring within avionics itself, such as in civil aviation for lateral navigation (LNAV) or the non-precision approach (NPA). However, standard RAIM may not meet the stricter aviation availability and integrity requirements for certain operations, e.g., precision approach flight phases, and also is not sufficient for on-ground vehicle integrity monitoring of several specific ITS applications. One possible way to more clearly distinguish anomalies in observed GNSS signals is to take advantage of time-delayed neural networks (TDNNs) to estimate useful information about the faulty characteristics, rather than simply using RAIM alone. Based on the performance evaluation, it was determined that this method can reliably detect flaws in navigation satellites significantly faster than RAIM alone, and it was confirmed that TDNN-based integrity monitoring using RAIM is an encouraging alternative to improve the integrity assurance level of RAIM in terms of GNSS anomaly detection.

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10

Blanch, Juan, Todd Walter, and Per Enge. "Optimal Positioning for Advanced Raim." Navigation 60, no. 4 (December 2013): 279–89. http://dx.doi.org/10.1002/navi.49.

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11

HWANG, PATRICK Y., and R. GROVER BROWN. "RAIM-FDE Revisited: A New Breakthrough In Availability Performance WithnioRAIM(Novel Integrity-Optimized RAIM)." Navigation 53, no. 1 (March 2006): 41–51. http://dx.doi.org/10.1002/j.2161-4296.2006.tb00370.x.

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12

Sun, Yuan. "RAIM-NET: A Deep Neural Network for Receiver Autonomous Integrity Monitoring." Remote Sensing 12, no. 9 (May 8, 2020): 1503. http://dx.doi.org/10.3390/rs12091503.

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With the continuous popularization of Global Navigation Satellite System (GNSS) in various applications, the performance requirement for integrity is also increasing, especially in the field of safety-of-life. Although the existing Receiver Autonomous Integrity Monitoring (RAIM) algorithm has been embedded in the GNSS receiver as a standard method, it might still suffer from small fault detection and delay alarm problem for time series fault models. In an effort to solve this problem, a Deep Neural Network (DNN) for RAIM, named RAIM-NET, is investigated in this paper. The main idea of RAIM-NET is to propose a combination of feature vector extraction and DNN model to improve the performance of integrity monitoring, with a problem specifically designed for loss function, obtaining the model parameters. Inspired by the powerful advantages of Recurrent Neural Network (RNN) in time series data processing, a multilayer RNN is applied to build the DNN model structure and improve the detection rate for small faults and reduce the alarm delay for the time series fault event. Finally, real GNSS data experiments are designed to verify the performance of RAIM-NET in fault detection and time delay for integrity monitoring.

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13

Lee, Ji Yun, Shabitha Arumugarajah, Dameng Lian, Natsumi Maehara, Aaron R. Haig, Rita S. Suri, Toru Miyazaki, and Lakshman Gunaratnam. "Recombinant apoptosis inhibitor of macrophage protein reduces delayed graft function in a murine model of kidney transplantation." PLOS ONE 16, no. 4 (April 23, 2021): e0249838. http://dx.doi.org/10.1371/journal.pone.0249838.

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Reperfusion injury following cold and warm ischemia (IRI) is unavoidable during kidney transplantation and contributes to delayed graft function (DGF) and premature graft loss. Death of tubular epithelial cells (TECs) by necrosis during IRI releases pro-inflammatory mediators (e.g. HMGB1), propagating further inflammation (necroinflammation) and tissue damage. Kidney Injury Molecule-1 (KIM-1) is a phagocytic receptor upregulated on proximal TECs during acute kidney injury. We have previously shown that renal KIM-1 protects the graft against transplant associated IRI by enabling TECs to clear apoptotic and necrotic cells, and that recognition of necrotic cells by KIM-1 is augmented in the presence of the opsonin, apoptosis inhibitor of macrophages (AIM). Here, we tested whether recombinant AIM (rAIM) could be used to mitigate transplant associated IRI. We administered rAIM or vehicle control to nephrectomised B6 mice transplanted with a single B6 donor kidney. Compared to grafts in vehicle-treated recipients, grafts from rAIM-treated mice exhibited significantly less renal dysfunction, tubular cell death, tissue damage, tubular obstruction, as well as local and systemic inflammation. Both mouse and human rAIM enhanced the clearance of necrotic cells by murine and human TECs, respectively in vitro. These data support testing of rAIM as a potential therapeutic agent to reduce DGF following kidney transplantation.

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14

Zhang, Pengfei, Pengyun Chen, Dan Song, and Guochao Fan. "Research on GNSS Receiver Autonomous Integrity Monitoring Method Based on M-Estimation." Mathematical Problems in Engineering 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/2563202.

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Receiver Autonomous Integrity Monitoring (RAIM) method is an effective means to provide integrity monitoring for users in time. In order to solve the misjudgment caused by the interference of gross error to the least squares algorithm, this paper proposes a RAIM method based on M-estimation for multiconstellation GNSS. Based on five programs, BDS, GPS/BDS, and GPS/BDS/GLONASS at the current stage, the future Beidou Global Navigation Satellite System, and the future GPS/BDS/GLONASS/Galileo system, the new RAIM method is compared with the traditional least squares method by simulation. The simulation results show that, with the increase of constellations, RAIM availability, fault detection probability, and fault identification probability will be improved. Under the same simulation conditions, the fault detection and identification probabilities based on M-estimation are higher than those based on least squares estimation, and M-estimation is more sensitive to minor deviation than least squares estimation.

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15

BROWN, R. GROVER. "A Baseline GPS RAIM Scheme and a Note on the Equivalence of Three RAIM Methods." Navigation 39, no. 3 (September 1992): 301–16. http://dx.doi.org/10.1002/j.2161-4296.1992.tb02278.x.

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16

Bhattacharyya, Susmita, and Dinesh Mute. "Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations." Sensors 20, no. 22 (November 18, 2020): 6606. http://dx.doi.org/10.3390/s20226606.

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This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors’ previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches—one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

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17

Kim, Ji Hye, Kwan Dong Park, and Du Sik Kim. "Fault Detection Performance Analysis of GNSS Integrity RAIM." Journal of Korean Society for Geospatial Information System 20, no. 3 (September 30, 2012): 49–56. http://dx.doi.org/10.7319/kogsis.2012.20.3.049.

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18

Yu, Li. "Research on GPS RAIM Algorithm Based on SIR Particle Filtering State Estimation and Smoothed Residual." Applied Mechanics and Materials 422 (September 2013): 196–203. http://dx.doi.org/10.4028/www.scientific.net/amm.422.196.

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The investigation presents a new approach based on SIR particle filtering state estimation and smoothed residual for GPS receiver autonomous integrity monitoring (RAIM), which adopted the difference value between the ideal observation values acquired by state estimation and the actual state observation values, and the log likelihood ratio (LLR) test based on probability density function of state-measurement was set up. Experimental results based on real GNSS data demonstrate that the algorithm can estimate the state precisely under non-Gaussian measurement noise, detect and isolate GPS satellite failures successfully and solve the performance degradation problem of RAIM algorithm based on Kalman filter. Therefore, experimental results validate the validity of SIR particle filtering state estimation and smoothed residual for RAIM.

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19

VAN DYKE, KAREN L. "RAIM Availability for Supplemental GPS Navigation." Navigation 39, no. 4 (December 1992): 429–43. http://dx.doi.org/10.1002/j.2161-4296.1992.tb02286.x.

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20

KOVACH, K., H. MAQUET, and D. DAVIS. "PPS RAIM Algorithms and Their Performance*." Navigation 42, no. 3 (September 1995): 515–29. http://dx.doi.org/10.1002/j.2161-4296.1995.tb01904.x.

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21

Farrell, James L. "GDOP and RAIM in Differential Operation." Navigation 48, no. 3 (September 2001): 195–203. http://dx.doi.org/10.1002/j.2161-4296.2001.tb00242.x.

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22

Joerger, Mathieu, Fang-Cheng Chan, and Boris Pervan. "Solution Separation Versus Residual-Based RAIM." Navigation 61, no. 4 (December 2014): 273–91. http://dx.doi.org/10.1002/navi.71.

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23

Lee, Young C. "A Position Domain Relative RAIM Method." IEEE Transactions on Aerospace and Electronic Systems 47, no. 1 (January 2011): 85–97. http://dx.doi.org/10.1109/taes.2011.5705661.

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24

Milner, Carl, and Boris Pervan. "Bounding Fault Probabilities for Advanced RAIM." IEEE Transactions on Aerospace and Electronic Systems 56, no. 4 (August 2020): 2947–58. http://dx.doi.org/10.1109/taes.2020.2969540.

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25

Hewitson, Steve, and Jinling Wang. "GNSS Receiver Autonomous Integrity Monitoring with a Dynamic Model." Journal of Navigation 60, no. 2 (April 20, 2007): 247–63. http://dx.doi.org/10.1017/s0373463307004134.

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Traditionally, GNSS receiver autonomous integrity monitoring (RAIM) has been based upon single epoch solutions. RAIM can be improved considerably when available dynamic information is fused together with the GNSS range measurements in a Kalman filter. However, while the Kalman filtering technique is widely accepted to provide optimal estimates for the navigation parameters of a dynamic platform, assuming the state and observation models are correct, it is still susceptible to unmodelled errors. Furthermore, significant deviations from the assumed models for dynamic systems may also occur. It is therefore necessary that the state estimation procedure is complemented with effective and reliable integrity measures capable of identifying both measurement and modelling errors. Within this paper, fundamental equations required for the effective detection and identification of outliers in a kinematic GNSS positioning and navigation system are described together with the reliability and separability measures. These quality measures are implemented using a Kalman filtering procedure formulated with Gauss-Markov models where the state estimates are derived from least squares principles. Detailed simulations and analyses have been performed to assess the impact of the dynamic information on GNSS RAIM with respect to outlier detection and identification, reliability and separability. The ability of the RAIM algorithms to detect and identify dynamic modelling errors is also investigated.

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Gu, Shouzhou, Jinzhong Bei, Chuang Shi, Yaming Dang, Zuoya Zheng, and Congcong Cui. "RAIM Algorithm Based on Fuzzy Clustering Analysis." Computer Modeling in Engineering & Sciences 119, no. 2 (2019): 281–93. http://dx.doi.org/10.32604/cmes.2019.04421.

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27

Yun, Ho, Deokhwa Han, Changdon Kee, Jiyun Lee, and Moon Beom Heo. "RAIM algorithm considering simultaneous multiple ramp failures." Aircraft Engineering and Aerospace Technology 87, no. 4 (July 6, 2015): 357–67. http://dx.doi.org/10.1108/aeat-07-2013-0126.

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28

Bhattacharyya, Susmita, and Demoz Gebre-Egziabher. "Kalman filter–based RAIM for GNSS receivers." IEEE Transactions on Aerospace and Electronic Systems 51, no. 3 (July 2015): 2444–59. http://dx.doi.org/10.1109/taes.2015.130585.

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29

Yan, Qin, Chenglin Cai, and Wei Jia. "Study on an optimized grouping RAIM method." Journal of Physics: Conference Series 1952, no. 4 (June 1, 2021): 042016. http://dx.doi.org/10.1088/1742-6596/1952/4/042016.

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30

Wang, Jinling, and Pieter B. Ober. "On the Availability of Fault Detection and Exclusion in GNSS Receiver Autonomous Integrity Monitoring." Journal of Navigation 62, no. 2 (March 12, 2009): 251–61. http://dx.doi.org/10.1017/s0373463308005158.

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Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) is essential for safety-of-life and liability critical applications. This paper discusses two fundamentally different ways to assess the integrity risk of an operation with RAIM, based on a different amount of information available: the expected (or average) performance that is computed using the GNSS models only and the real-time (or actual) performance, which also uses information on the internal status of a GNSS receiver. It is shown both theoretically and by simulation that the real-time integrity risk significantly exceeds the expected risk after the detection and exclusion of a failing satellite. Therefore, while most published RAIM algorithms base their performance assessment on the expected performance only, this is only correct when the requirements allow the risk evaluation to be averaged over multiple operations. However, when the GNSS integrity requirement is to be applied on a ‘per operation’ basis, real-time integrity measures are more appropriate.

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Joerger, Mathieu, Steven Langel, and Boris Pervan. "Integrity Risk Minimisation in RAIM Part 2: Optimal Estimator Design." Journal of Navigation 69, no. 4 (March 4, 2016): 709–28. http://dx.doi.org/10.1017/s0373463315000995.

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This paper is the second part of a two-part research effort to find the optimal detector and estimator that minimise the integrity risk in Receiver Autonomous Integrity Monitoring (RAIM). Part 1 shows that for realistic navigation requirements, the solution separation RAIM method can approach the optimal detection region when using a least-squares estimator. This paper constitutes Part 2. It presents new methods to design Non-Least-Squares (NLS) estimators, which, in exchange for a slight increase in nominal positioning error, can substantially lower the integrity risk. A first method is formulated as a multi-dimensional minimisation problem, which directly minimises integrity risk, but can only be solved using a time-consuming iterative process. Parity space representations are then exploited to develop a computationally-efficient, near-optimal NLS-estimator-design method. Performance analyses for an example multi-constellation Advanced RAIM (ARAIM) application show that this new method enables significant integrity risk reduction in real-time implementations where computational resources are limited.

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32

Wang, Wenbo, and Ying Xu. "A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation." Sensors 20, no. 18 (September 21, 2020): 5407. http://dx.doi.org/10.3390/s20185407.

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The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

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Chan, Fang-Cheng, Mathieu Joerger, Samer Khanafseh, and Boris Pervan. "Bayesian Fault-Tolerant Position Estimator and Integrity Risk Bound for GNSS Navigation." Journal of Navigation 67, no. 5 (April 17, 2014): 753–75. http://dx.doi.org/10.1017/s0373463314000241.

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The advent of multiple Global Navigation Satellite System (GNSS) constellations will result in a considerable increase in the number of satellites for positioning worldwide. This substantial improvement in measurement redundancy has the potential to radically advance receiver autonomous integrity monitoring (RAIM) performance. However, regardless of the number of satellites, the performance of existing RAIM methods is sensitive to the assumed prior probabilities of individual fault hypotheses. In this paper, a new method is developed using Bayes’ theorem to generate upper bounds on posterior probabilities of individual fault hypotheses given current user measurements. These bounds are used in a Bayesian fault-tolerant position estimator (FTE) that minimizes integrity risk. The detection test statistic is a measurement-based integrity risk bound, which is directly compared with a pre-specified risk requirement. The associated challenge of quantifying continuity risk is resolved using a bounding approach, which is also detailed in this work. The new Bayesian FTE method is shown to be more robust to uncertainty in prior probability of fault occurrence than existing RAIM methods.

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Teunissen, Peter, Davide Imparato, and Christian Tiberius. "Does RAIM with Correct Exclusion Produce Unbiased Positions?" Sensors 17, no. 7 (June 26, 2017): 1508. http://dx.doi.org/10.3390/s17071508.

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35

Milner, Carl D., and Washington Y. Ochieng. "Weighted RAIM for APV: The Ideal Protection Level." Journal of Navigation 64, no. 1 (November 26, 2010): 61–73. http://dx.doi.org/10.1017/s0373463310000342.

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International standards require the use of a weighted least-squares approach to onboard Receiver Autonomous Integrity Monitoring (RAIM). However, the protection levels developed to determine if the conditions exist to perform a measurement check (i.e. failure detection) are not specified. Various methods for the computation of protection levels exist. However, they are essentially approximations to the complex problem of computing the worst-case missed detection probability under a weighted system. In this paper, a novel approach to determine this probability at the worst-case measurement bias is presented. The missed detection probabilities are then iteratively solved against the integrity risk requirement in order to derive an optimal protection level for the operation. It is shown that the new method improves availability by more than 30% compared to the baseline weighted RAIM algorithm.A version of this paper was first presented at the US Institute of Navigation (ION) GNSS 2009 Conference in Savannah, Georgia.

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36

Yun, Ho, and Changdon Kee. "Multiple-hypothesis RAIM algorithm with an RRAIM concept." Aircraft Engineering and Aerospace Technology 86, no. 1 (December 20, 2013): 26–32. http://dx.doi.org/10.1108/aeat-08-2012-0131.

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37

Yoo, Chang-Sun, Iee-ki Ahn, and Sang-Jeong Lee. "Two-Failure Gps Raim by Parity Space Approach." Journal of the Korean Society for Aeronautical & Space Sciences 31, no. 6 (August 1, 2003): 52–60. http://dx.doi.org/10.5139/jksas.2003.31.6.052.

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Blanch, Juan, Todd Walter, and Per Enge. "Protection Levels after Fault Exclusion for Advanced RAIM." Navigation 64, no. 4 (December 2017): 505–13. http://dx.doi.org/10.1002/navi.210.

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Blanch, Juan, Todd Walter, Per Enge, Stefan Wallner, Francisco Amarillo Fernandez, Riccardo Dellago, Rigas Ioannides, et al. "Critical Elements for a Multi-Constellation Advanced RAIM." Navigation 60, no. 1 (March 2013): 53–69. http://dx.doi.org/10.1002/navi.29.

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Astapov, Alexander Nikolaevich. "INTEGRITY CONTROL OF MEASURED PSEUDORANGE USING RAIM ALGORITHM." V mire nauchnykh otkrytiy, no. 6.1 (November 22, 2014): 481. http://dx.doi.org/10.12731/wsd-2014-6.1-11.

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Yun, Ho, and Changdon Kee. "Multiple-Hypothesis RAIM Algorithm with an RRAIM Concept." Journal of Korea Navigation Institute 16, no. 4 (August 31, 2012): 593–601. http://dx.doi.org/10.12673/jkoni.2012.16.4.593.

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42

Blanch, Juan, Todd Walker, Per Enge, Young Lee, Boris Pervan, Markus Rippl, Alex Spletter, and Victoria Kropp. "Baseline advanced RAIM user algorithm and possible improvements." IEEE Transactions on Aerospace and Electronic Systems 51, no. 1 (January 2015): 713–32. http://dx.doi.org/10.1109/taes.2014.130739.

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43

Hewitson, Steve, and Jinling Wang. "GNSS receiver autonomous integrity monitoring (RAIM) performance analysis." GPS Solutions 10, no. 3 (December 16, 2005): 155–70. http://dx.doi.org/10.1007/s10291-005-0016-2.

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SONG, Dan, Chuang SHI, Zhipeng WANG, Cheng WANG, and Guifei JING. "Correlation-weighted least squares residual algorithm for RAIM." Chinese Journal of Aeronautics 33, no. 5 (May 2020): 1505–16. http://dx.doi.org/10.1016/j.cja.2019.12.012.

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Luo, Silong, Li Wang, Rui Tu, Weiqi Zhang, Jiancheng Wei, and Cunting Chen. "Satellite selection methods for multi-constellation advanced RAIM." Advances in Space Research 65, no. 5 (March 2020): 1503–17. http://dx.doi.org/10.1016/j.asr.2019.12.015.

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Zhang, Qieqie, Long Zhao, and Jianhua Zhou. "Improved Method for Single and Multiple GNSS Faults Exclusion based on Consensus Voting." Journal of Navigation 72, no. 04 (February 27, 2019): 987–1006. http://dx.doi.org/10.1017/s0373463318001133.

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Receiver Autonomous Integrity Monitoring (RAIM) provides an integrity service for Global Navigation Satellite Systems (GNSS). The conventional RAIM algorithm is based on the assumption of a single fault and typically uses the forward-backward method, which is based on thew-test or correlation analysis methods, to exclude the faults. It is suitable for single fault detection and exclusion, while it can lead to inefficiency, can be misleading and can even fail in the exclusion of multiple faults. To solve this problem, an improved method based on consensus voting of thew-test and correlation analysis methods is presented. To verify the performance of the improved method, tests using Global Positioning System (GPS)/BeiDou System (BDS) data have been carried out in comparison with the conventional methods in terms of false and correct faults exclusion rate and computational complexity in the case of a different number of faults. Results show that the improved method has almost the same correct exclusion rate compared to the conventional RAIM in the case of a single fault. It is worth noting that the improved method has a higher correct exclusion probability and computational efficiency as well as a lower possibility of false exclusion in the case of multiple faults.

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Nowak, Aleksander. "The Proposal to “Snapshot” Raim Method for Gnss Vessel Receivers Working in Poor Space Segment Geometry." Polish Maritime Research 22, no. 4 (December 1, 2015): 3–8. http://dx.doi.org/10.1515/pomr-2015-0063.

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Abstract Nowadays, we can observe an increase in research on the use of small unmanned autonomous vessel (SUAV) to patrol and guiding critical areas including harbours. The proposal to “snapshot” RAIM (Receiver Autonomous Integrity Monitoring) method for GNSS receivers mounted on SUAV operating in poor space segment geometry is presented in the paper. Existing “snapshot” RAIM methods and algorithms which are used in practical applications have been developed for airborne receivers, thus two main assumptions have been made. The first one is that the geometry of visible satellites is strong. It means that the exclusion of any satellite from the positioning solution don’t cause significant deterioration of Dilution of Precision (DOP) coefficients. The second one is that only one outlier could appear in pseudorange measurements. In case of SUAV operating in harbour these two assumptions cannot be accepted. Because of their small dimensions, GNSS antenna is only a few decimetres above sea level and regular ships, buildings and harbour facilities block and reflect satellite signals. Thus, different approach to “snapshot” RAIM is necessary. The proposal to method based on analyses of allowable maximal separation of positioning sub-solutions with using some information from EGNOS messages is described in the paper. Theoretical assumptions and results of numerical experiments are presented.

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Wang, Huibin, Yongmei Cheng, Cheng Cheng, Song Li, and Zhenwei Li. "Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications." Remote Sensing 13, no. 9 (April 29, 2021): 1725. http://dx.doi.org/10.3390/rs13091725.

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Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.

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Feng, S., W. Y. Ochieng, and R. Mautz. "An Area Computation Based Method for RAIM Holes Assessment." Journal of Global Positioning Systems 5, no. 1&2 (December 31, 2006): 11–16. http://dx.doi.org/10.5081/jgps.5.1.11.

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Gratton, Livio, Mathieu Joerger, and Boris Pervan. "Carrier Phase Relative RAIM Algorithms and Protection Level Derivation." Journal of Navigation 63, no. 2 (February 23, 2010): 215–31. http://dx.doi.org/10.1017/s0373463309990403.

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The concept of Relative Receiver Autonomous Integrity Monitoring (RRAIM) using time differential carrier phase measurements is investigated in this paper. The precision of carrier phase measurements allows for mitigation of integrity hazards by implementing RRAIM monitors with tight thresholds without significantly affecting continuity. In order to avoid the need for cycle ambiguity resolution, time differences in carrier phase measurements are used as the basis for detection. In this work, we examine RRAIM within the context of the GNSS Evolutionary Architecture Study (GEAS), which explores potential architectures for aircraft navigation utilizing the satellite signals available in the mid-term future with GPS III. The objectives of the GEAS are focused on system implementations providing worldwide coverage to satisfy LPV-200 operations, and potentially beyond. In this work, we study two different GEAS implementations of RRAIM. General formulas are derived for positioning, fault detection, and protection level generation to meet a given set of integrity and continuity requirements.

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