Accurate Distance Estimation for RSS Localization With Statistical Path Loss Exponent Model

In wireless networks, propagation models are used to assess the received power signal and estimate the propagation channel. These models depend on the pathloss exponent (PLE) which is one of the main parameters to characterize the propagation environment. Indeed, in the wireless channel, the path loss exponent has a strong impact on the quality of the links and must therefore be estimated with precision for an efficient design and operation of the wireless network. This paper addresses the issue of path loss exponents estimation for mobile networks in four outdoor environments. This study is based on measurements carried out in four outdoor environments at the frequency of 2600 MHz within a bandwidth of 70 MHz. It evaluates the path loss exponent, and the impact of obstacles present in the environments. The parameters of the propagation model determined from the measurements show that the average power of the received signal decreases logarithmically with the distance. We obtained path loss exponents values of 4.8, 3.53, 3.6 and 3.99 for the site 1, site 2, site 3 and site 4, respectively. Clearly the density of the obstacles has an impact on the path loss exponents and our study shows that the received signal decrease faster as the transmitter and receiver separation in the dense environments.

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Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning

IEEE Access ◽

10.1109/access.2019.2931072 ◽

2019 ◽

Vol 7 ◽

pp. 101366-101375 ◽

Cited By ~ 3

Author(s):

Hasan F. Ates ◽

Syed Muhammad Hashir ◽

Tuncer Baykas ◽

Bahadir K. Gunturk

Keyword(s):

Deep Learning ◽

Satellite Images ◽

Path Loss ◽

Path Loss Exponent

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

Sensors ◽

10.3390/s20226582 ◽

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.

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On the maximum likelihood estimation of the ToA under an imperfect path loss exponent

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/1687-1499-2013-158 ◽

2013 ◽

Vol 2013 (1) ◽

Author(s):

Isabel Valera ◽

Bamrung Tau Sieskul ◽

Joaquín Míguez

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Path Loss ◽

Likelihood Estimation ◽

Path Loss Exponent

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Factorial Design Analysis for Localization Algorithms

Applied Sciences ◽

10.3390/app8122654 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2654

Author(s):

Joaquin Mass-Sanchez ◽

Erica Ruiz-Ibarra ◽

Ana Gonzalez-Sanchez ◽

Adolfo Espinoza-Ruiz ◽

Joaquin Cortez-Gonzalez

Keyword(s):

Statistical Analysis ◽

Noise Level ◽

Performance Metrics ◽

Extreme Values ◽

Path Loss ◽

Propagation Model ◽

Path Loss Exponent ◽

Localization Algorithms ◽

Accuracy And Precision ◽

The Impact

Localization is a fundamental problem in Wireless Sensor Networks, as it provides useful information regarding the detection of an event. There are different localization algorithms applied in single-hop or multi-hop networks; in both cases their performance depends on several factors involved in the evaluation scenario such as node density, the number of reference nodes and the log-normal shadowing propagation model, determined by the path-loss exponent (η) and the noise level (σdB) which impact on the accuracy and precision performance metrics of localization techniques. In this paper, we present a statistical analysis based on the 2k factorial methodology to determine the key factors affecting the performance metrics of localization techniques in a single-hop network to concentrate on such parameters, thus reducing the amount of simulation time required. For this proposal, MATLAB simulations are carried out in different scenarios, i.e., extreme values are used for each of the factors of interest and the impact of the interaction among them in the performance metrics is observed. The simulation results show that the path-loss exponent (η) and noise level (σdB) factors have the greatest impact on the accuracy and precision metrics evaluated in this study. Based on this statistical analysis, we recommend estimating the propagation model as close to reality as possible to consider it in the design of new localization techniques and thus improve their accuracy and precision metrics.

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