Loose and Tight GNSS/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios

Falco, Gianluca; Pini, Marco; Marucco, Gianluca

doi:10.3390/s17020255

Cited by 172 publications

(104 citation statements)

References 20 publications

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“…The complementary features of GNSS and INS make them a good choice for integration especially when GNSS observability is poor. Advantages and performance of loosely, tightly and ultra-tightly coupled GNSS/INS integration methods have been studied and reported by a number of researchers [24,25,26]. In a typical GNSS-INS system, an IMU with three orthogonally mounted accelerometers and three gyroscopes is used.…”

Section: Introductionmentioning

confidence: 99%

Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation

Broumandan

Lachapelle

2018

Sensors

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Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated.

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Section: Introductionmentioning

confidence: 99%

Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation

Broumandan

Lachapelle

2018

Sensors

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“…Inertial measurement units (IMU) measure vehicle accelerations and turn rates that can be combined with the GNSS data to improve the trajectory estimation. This has been demonstrated in several studies [12][13][14][15][16][17]. Depending on the processed GNSS data, different variants can be distinguished.…”

Section: Introductionmentioning

confidence: 85%

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

et al. 2020

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Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.

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“…This study aims to present a credible new special strategies resampling algorithm which adapts noise measurement and particle number. This algorithm can be used to overcome the problem of sample impoverishment in different environments and has the robustness to be used in different mobile inertial navigation systems [22] [23]. The results of the study could also significantly contribute to modernising current location detection systems and provide useful findings for use in other inertial-based positioning systems studies [24][25][26] [27].…”

Section: Objectivementioning

confidence: 99%

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

Bejuri*,

Mohamad,

Omar

et al. 2019

IJITEE

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The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

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Loose and Tight GNSS/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios

Cited by 172 publications

References 20 publications

Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation

Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems

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