Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area

2017 ◽  
Vol 2 (3) ◽  
pp. 1518-1524 ◽  
Author(s):  
Hyungjin Kim ◽  
Bingbing Liu ◽  
Chi Yuan Goh ◽  
Serin Lee ◽  
Hyun Myung
Keyword(s):  
Data Fusion ◽  
Particle Filter ◽  
Urban Area ◽  
Large Scale ◽  
Vehicle Localization ◽  
Intensity Information ◽  
Entropy Weighted

scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals Measuring Community Green Inequity: A Fine-Scale Assessment of Beijing Urban Area

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1197
Author(s):  
Yuyang Zhang ◽  
Qilin Wu ◽  
Lei Wu ◽  
Yan Li
Keyword(s):  
Urban Area ◽  
Evaluation System ◽  
Large Scale ◽  
Green Space ◽  
Community Gardens ◽  
Data Availability ◽  
Green Spaces ◽  
Fine Scale ◽  
Macro Scale ◽  
Community Residents

Green space exposure is beneficial to the physical and mental health of community residents, but the spatial distribution of green space is inequitable. Due to data availability, green equality or justice studies typically use administrative units as contextual areas to evaluate green spaces exposure, which is macro-scale and may lead to biased estimates as it ignores fine-scale green spaces (e.g. community gardens, lawns), that community residents are more frequently exposed to. In this study, we used the community as the unit of analysis, considered the green exposure of community residents in their daily social and physical activities, obtained data on three types of green spaces including fine-scale green spaces in the communities, surrounding large-scale parks and streetscape images. We propose a series of metrics for assessing community green equity, including a total of 11 metrics in three major categories of morphology, visibility and accessibility and applied them to 4,544 communities in Beijing urban area. Through spatial visualization, spatial clustering, radar plots, and correlation analysis, we comprehensively analyzed the equity of green space at the community scale, identified the cold and hot spots of homogeneity, and then analyzed the equity of green space among regions under the urbanization process. The measurement results of these metrics showed that there are large differences and complementarities between different categories of metrics, but similarities exist between metrics of the same category. The proposed methodology represents the development of a green space evaluation system that can be used by decision makers and urban green designers to create and maintain more equitable community green spaces. In addition, the large-scale, comprehensive and fine-scale green space measurement of this study can be combined with other studies such as public health and environmental pollution in the future to obtain more comprehensive conclusions and better guide the construction and regeneration of green spaces.

scholarly journals A Novel Voronoi Based Particle Filter for Multi-Sensor Data Fusion

2012 ◽  
Vol 03 (11) ◽  
pp. 1787-1794
Author(s):  
Vani Cheruvu ◽  
Priyanka Aggarwal ◽  
Vijay Devabhaktuni
Keyword(s):  
Data Fusion ◽  
Particle Filter ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

scholarly journals Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10015-10027 ◽  
Author(s):  
Adnan Akbar ◽  
George Kousiouris ◽  
Haris Pervaiz ◽  
Juan Sancho ◽  
Paula Ta-Shma ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Probabilistic Data ◽  
Iot Applications

scholarly journals Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2792 ◽  
Author(s):  
Hyunseok Kim ◽  
Dongjun Suh
Keyword(s):  
Particle Swarm Optimization ◽  
Data Fusion ◽  
Large Scale ◽  
Synergistic Effects ◽  
Particle Swarm ◽  
Sensor Data ◽  
Fusion Model ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

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