A Review of the Evolution of the Integrity Methods Applied in GNSS

Zabalegui, Paul; Miguel, Gorka De; Pérez, A.; Mendizábal, Jaizki; Goya, Jon; Adin, Iñigo

doi:10.1109/access.2020.2977455

Cited by 53 publications

(22 citation statements)

References 49 publications

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“…In the literature, a great variety of algorithms to evaluate a system’s integrity can be found that stem from different research areas [ 23 , 24 ]. Since the application in this work is in VDSE or localization functions, we focus in the following on approaches from the domain of navigation, especially localization functions based on a sensor fusion using a GNSS receiver, and adopt the nomenclature from this domain.…”

Section: State Of the Artmentioning

confidence: 99%

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Gottschalg

Leinen

2021

Sensors

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High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario.

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Section: State Of the Artmentioning

confidence: 99%

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Gottschalg

Leinen

2021

Sensors

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“…The integrity concept includes this second feature. It is defined as the measure of trust on the correctness of the location estimates applied to navigation systems [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

Hage¹,

Xu²,

Ibañez‐Guzmán

2022

IEEE Trans. Intell. Transport. Syst.

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Localization integrity consists in providing a realtime measure of the level of trust to be placed in the localization estimates as vehicles operate. It provides a means of knowing whether position estimates are usable for navigation purposes. This paper formalizes the integrity concept and its underlying principles. Vehicles operate in different navigation environments, and so multiple sensors are used to ensure the required performance. Different sources of error exist. They must be bounded according to the acceptable level of risk for the application.This paper presents a generic approach for addressing integrity. It combines measurement rejection (for measurements considered to be faults) and position error characterization. For this purpose, a multi-sensor data fusion with a Fault Detection and Exclusion algorithm is constituted using a bank of information filters. These filters allow detected faults to be isolated without any prior assumption regarding the number of simultaneous errors. In addition, external integrity is expressed as a Protection Level of the localization solution. It uses a Student's t-distribution in order to bound the distribution of the position error applicable to small integrity risks after a learning step. The approach is tested on data acquired on public roads using an experimental vehicle equipped with off-the-shelf proprioceptive and exteroceptive sensors together with an HD map. The results obtained validate the proposed approach.

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“…A comparison of different FDE methods is provided by Zabalegui et al. (2020) and Knight and Wang (2009) (e.g., subset testing, sequential local test, forward‐backwards test, Danish method). J. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Gabela

Kealy²,

Hedley

et al. 2021

Navigation

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A Review of the Evolution of the Integrity Methods Applied in GNSS

Cited by 53 publications

References 49 publications

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

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