Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment

Işık, Oğuz; Hong, Ju-Hyeon; Petrunin, Ivan; Tsourdos, Antonios

doi:10.3390/robotics9030066

Cited by 46 publications

(23 citation statements)

References 30 publications

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“…If the GPS receiver communicates with satellites spread throughout the sky, the calculated position will be more accurate, and the DOP value will be low. However, when satellites are close to each other, the calculated position will be less accurate, and the DOP value will be high [ 34 , 35 ]. In the following table, the DOP value rating is shown ( Table 6 ).…”

Section: Data Collection and Processingmentioning

confidence: 99%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision.

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Section: Data Collection and Processingmentioning

confidence: 99%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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show abstract

“…So, regardless of the altitude level, we assumed that the buildings in the surrounding environment are tall enough to be considered as obstructions in flight path optimization. For the heatmaps concerning the number of visible satellites and HDOP, appropriate thresholds were used, considering the findings in [17]. As a result, the path planning algorithm would penalize use of the areas where these thresholds (NVS<4 and HDOP>3) are exceeded.…”

Section: Figure 4 Multipath Effect Simulationmentioning

confidence: 99%

UAV Path Planning Optimization based on GNSS Quality and Mission Requirements

Nanos

Işık

Moreno

et al. 2021

AIAA Scitech 2021 Forum

Self Cite

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One of the most crucial factors for the overall success of an Unmanned Aerial Vehicle (UAV) mission is navigation performance, which is severely affected in Global Navigation Satellite Systems (GNSS) challenging environments. A solution to this problem could come through path planning optimization. This paper investigates the impact that GNSS quality information included in the UAV path planning process would have on the overall UAV mission success rate (MSR) when flying through an urban canyon. Number of visible satellites and Horizontal Dilution of Precision (HDOP) in addition to mission-specific requirements are given as input to the Particle Swarm Optimization (PSO) algorithm to calculate the optimal path for two cases. One includes the GNSS observables, and the other does not. Optimal paths for three different altitudes are obtained. All paths are simulated by a GNSS signal simulator, including a comprehensive multipath model. GNSS data are collected by a hardware receiver for analysis of the UAV positioning error and GNSS availability. Mission failures cases are defined accordingly, and the overall mission success rate (MSR) of each scenario is assessed. By analyzing the findings, it is concluded that in 83% of cases, the path planning process that included GNSS information was able to increase the MSR. Also, the increase in MSR was bigger when flying at low altitude.

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“…According to the classification from [63], measured DOPS can be rated on the average as ideal (HDOP and VDOP < 1) or good (PDOP and GDOP < 5). We considered the positional measurements accurate enough to produce a digital terrain model to be employed in the energy and mass models.…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

Proximal-Sensing-Powered Modelling of Energy-Water Fluxes in a Vineyard: A Spatial Resolution Analysis

et al. 2021

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Spatial resolution is a key parameter in energy–water surface flux modelling. In this research, scale effects are analyzed on fluxes modelled with the FEST-EWB model, by upscaling both its inputs and outputs separately. The main questions are: (a) if high-resolution remote sensing images are necessary to accurately model a heterogeneous area; and (b) whether and to what extent low-resolution modelling provides worse/better results than the upscaled results of high-resolution modelling. The study area is an experimental vineyard field where proximal sensing images were obtained by an airborne platform and verification fluxes were measured via a flux tower. Modelled fluxes are in line with those from alternative energy-balance models, and quite accurate (NSE = 0.78) with respect to those measured in situ. Field-scale evapotranspiration has resulted in both the tested upscaling approaches (with relative error within ±30%), although fewer pixels available for low-resolution calibration may produce some differences. When working at low resolutions, the model has produced higher relative errors (20% on average), but is still within acceptable bounds. This means that the model can produce high-quality results, partially compensating for the loss in spatial heterogeneity associated with low-resolution images.

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Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment

Cited by 46 publications

References 30 publications

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

UAV Path Planning Optimization based on GNSS Quality and Mission Requirements

Proximal-Sensing-Powered Modelling of Energy-Water Fluxes in a Vineyard: A Spatial Resolution Analysis

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