scholarly journals Received signal strength indication‐based localisation method with unknown path‐loss exponent for HVDC electric field measurement

High Voltage ◽  
2017 ◽  
Vol 2 (4) ◽  
pp. 261-266 ◽  
Author(s):  
Xiaolan Gu ◽  
Yong Cui ◽  
Qiusheng Wang ◽  
Haiwen Yuan ◽  
Luxing Zhao ◽  
...  
Keyword(s):  
Electric Field ◽  
Field Measurement ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Electric Field Measurement ◽  
Path Loss Exponent ◽  
Received Signal Strength Indication

scholarly journals Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6582
Author(s):  
SeYoung Kang ◽  
TaeHyun Kim ◽  
WonZoo Chung
Keyword(s):  
Kalman Filter ◽  
Path Loss ◽  
Target Position ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Target Localization ◽  
Wireless Sensor ◽  
Transmit Power ◽  
Path Loss Exponent ◽  
Localization Algorithms

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

scholarly journals Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
José Vallet García
Keyword(s):  
Empirical Studies ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Model Parameters ◽  
Normal Model ◽  
Path Loss Exponent ◽  
Localization Algorithms ◽  
Log Normal

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.

Atomic 2D electric field imaging of a Yagi–Uda antenna near-field using a portable Rydberg-atom probe and measurement instrument

2020 ◽  
Vol 9 (5) ◽  
pp. 305-312
Author(s):  
Ryan Cardman ◽  
Luís F. Gonçalves ◽  
Rachel E. Sapiro ◽  
Georg Raithel ◽  
David A. Anderson
Keyword(s):  
Electric Field ◽  
Electric Fields ◽  
Field Measurement ◽  
Near Field ◽  
Measurement Instrument ◽  
Rydberg Atom ◽  
Field Measurements ◽  
Atom Probe ◽  
Electric Field Measurement ◽  
Measurement Uncertainties

AbstractWe present electric field measurements and imaging of a Yagi–Uda antenna near-field using a Rydberg atom–based radio frequency electric field measurement instrument. The instrument uses electromagnetically induced transparency with Rydberg states of cesium atoms in a room-temperature vapor and off-resonant RF-field–induced Rydberg-level shifts for optical SI-traceable measurements of RF electric fields over a wide amplitude and frequency range. The electric field along the antenna boresight is measured using the atomic probe at a spatial resolution of ${\lambda }_{RF}/2$ with electric field measurement uncertainties below 5.5%, an improvement to RF measurement uncertainties provided by existing antenna standards.

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