Urban area
            <scp>GNSS</scp>
            in‐car‐jammer localization based on pattern recognition

Lyu, Dongliang; Chen, Xin; Wen, Fei; Pei, Ling; He, Di

doi:10.1002/navi.301

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The main finding of the study was that terrain charasteristics can mitigate the effects of a ground-based jammer. A localisation method in urban canyons for in-car jammers, which work on monitoring networks and follow the principle of pattern recognition, is described in Lyu et al [34]. In Kuusniemi and Airos [24], the effect of single-frequency jammers on consumer grade receivers was analysed.…”

Section: Previous Researchmentioning

confidence: 99%

Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming

Bažec

Dimc

Pavlovčič-Prešeren

2020

Sensors

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Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 frequency jammer. A suitable area for testing was established in Slovenia. Nine receivers from different manufacturers were under consideration in this study. While positioning, intentional 3-minute jammings were performed by a jammer that was located statically at different distances from receivers. Furthermore, kinematic disturbances were performed using a jammer placed in a vehicle that passed the testing area at various speeds. An analysis of different scenarios indicated that despite the use of an L1/E1 jammer, the GLONASS (Russian: Globalnaya Navigatsionnaya Sputnikovaya Sistema) and Galileo signals were also affected, either due to the increased carrier-to-noise-ratio (C/N0) or, in the worst cases, by a loss-of-signal. A jammer could substantially affect the position, either with a lack of any practical solution or even with a wrong position. Maximal errors in the carrier-phase positions, which should be considered a concern for geodesy, differed by a few metres from the exact solution. The factor that completely disabled the signal reception was the proximity of a jammer, regardless of its static or kinematic mode.

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Section: Previous Researchmentioning

confidence: 99%

Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming

Bažec

Dimc

Pavlovčič-Prešeren

2020

Sensors

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“…Its performance is tested by two simulated interference scenarios: the urban canyon and the urban residential area. Results show that the algorithm provides better localization reliability and smaller position errors compared with traditional localization methods …”

Section: Congratulationsmentioning

confidence: 99%

“…Results show that the algorithm provides better localization reliability and smaller position errors compared with traditional localization methods. 4 PERFORMANCE ASSESSMENT OF 3D-MAPPING-AIDED GNSS PART 1: ALGORITHMS, USER EQUIP-MENT AND REVIEW a full performance assessment of 3D-mapping-aided (3DMA) GNSS in dense urban areas is presented. This first part of a two-part paper focuses on the effects of algorithm design and user equipment, based on data collected in London using Leica Viva GS15 and u-blox EVK M8T GNSS receivers, and a Nexus 9 Android tablet.…”

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confidence: 99%

Editorial: In this issue

Pervan

2019

NAVIGATION

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A Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network

Fan

Wang

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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As one of the most critical wind power generation components, wind turbine blades play a key role in generating wind power. Aiming at the problem that the wind turbine blades are subjected to multiple loads in combination, the crack problem is easy to occur. Through the analysis of the macroscopic expansion mechanism and microscopic damage mechanism of short cracks and main cracks, the hidden relationship between crack appearance and damage nature is deeply explored. A fault diagnosis algorithm for wind turbine blades established on the basis of the BP neural network is raised. On the multi-discriminator fusion network structure, BP neural network algorithm is used to train the multi-feature sample data including wind turbine blades, so that the network parameters tend to convergence and gradually approach the real tag. The experimental analysis shows that the algorithm effectively diagnoses and evaluates the damage degree of the blade structure, and has a high recall rate and accuracy, which proves the effectiveness and robustness of the algorithm.

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Urban area GNSS in‐car‐jammer localization based on pattern recognition

Cited by 4 publications

References 22 publications

Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming

Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming

Editorial: In this issue

A Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network

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