scholarly journals A Radio Propagation Model for Mixed Paths in Amazon Environments for the UHF Band

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
L. E. C. Eras ◽  
Diego K. N. da Silva ◽  
Fabrício B. Barros ◽  
Luís M. Correia ◽  
G. P. S. Cavalcante
Keyword(s):  
Electric Fields ◽  
Incidence Angle ◽  
Measurement Data ◽  
Propagation Model ◽  
Radio Propagation ◽  
Digital Television ◽  
Field Calculation ◽  
Radio Propagation Model ◽  
City Water ◽  
Uhf Band

This paper presents a radio propagation model for the UHF band that is designed for an outdoor scenario in the Amazon region of Brazil and comprises city, water, and forest environments. The model is designed for the Mobile and Home Digital Television (M-DTV and H-DTV) services. In the case of M-DTV, the electric field is calculated at user height, while for H-DTV it considers a fixed antenna on houses roofs. The field calculation is based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). The results for M-DTV show a good agreement with measurement data in an Amazonian city (Belém) for 521 MHz. Different parameters of the proposed model are analyzed: the transition zone city-water, the level of water, the incidence angle in the forest, and electrical parameters for forest. Finally, the comparison that was made between the electric fields for H-DTV and M-DTV shows a difference of up to 19 dB.

Research on Impact of Radio Irregularity on Self-Localization of WSN

2011 ◽  
Vol 474-476 ◽  
pp. 2161-2166 ◽  
Author(s):  
Jia Zhang ◽  
Hai Yan Zhang ◽  
Jin Na Lv ◽  
Li Qiang Yin
Keyword(s):  
Propagation Model ◽  
Radio Propagation ◽  
Coarse Grained ◽  
Wireless Sensor ◽  
Position Information ◽  
Localization Algorithms ◽  
Radio Propagation Model ◽  
Radio Irregularity ◽  
Almost All ◽  
The Impact

Localization is a vital foundation work in Wireless Sensor Network (WSN). Almost all of location algorithms at present need the position information of reference nodes to locate the unknown nodes. But most of algorithms assume an idealistic radio propagation model that is far from the reality. This will lead to obvious difference compared with real localization of WSN. In this paper we investigate the impact of radio irregularity on the localization algorithms performance in WSN. We introduce the Radio Irregularity Model (RIM) which is established upon empirical data. With this model, this paper analyzes the impact of radio irregularity on localization algorithms. We compare three typical coarse-grained localization algorithms: APIT, Centroid and DV-HOP in simulated realistic settings. Our experimental results show that radio irregularity has a significant impact on some main evaluation aspects of localization algorithms. Some interesting phenomena is worthy of further study.

scholarly journals MapFuse: Complete and Realistic 3D Modelling

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Michiel Aernouts ◽  
Ben Bellekens ◽  
Maarten Weyn
Keyword(s):  
Propagation Model ◽  
Radio Propagation ◽  
Initial Model ◽  
Registration Errors ◽  
Environment Model ◽  
Robust Model ◽  
Vision Sensors ◽  
Volumetric Model ◽  
Radio Propagation Model ◽  
Slam Algorithm

Validating a 3D indoor radio propagation model that simulates the signal strength of a wireless device can be a challenging task due to an incomplete or a faulty environment model. In this paper, we present a novel method to simulate a complete indoor environment that can be used for evaluating a radio propagation model efficiently. In order to obtain a realistic and robust model of the full environment, the OctoMap framework is applied. The system combines the result of a SLAM algorithm and secondly a simple initial model of the same environment in a probabilistic way. Due to this approach, sensor noise and accumulated registration errors are minimised. Furthermore, in this article, we evaluate the merging approach with two SLAM algorithms, three vision sensors, and four datasets, of which one is publicly available. As a result, we have created a complete volumetric model by merging an initial model of the environment with the result of RGB-D SLAM based on real sensor measurements.

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