Log-Distance Path Loss Model-Based Relative Distance Estimation Method

2016 ◽  
Vol 22 (9) ◽  
pp. 2558-2561 ◽  
Author(s):  
Sang-Geol Lee ◽  
Yunsick Sung
Keyword(s):  
Estimation Method ◽  
Path Loss ◽  
Distance Estimation ◽  
Relative Distance ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

2018 ◽  
pp. 712-722 ◽  
Author(s):  
Huda Ali Hashim ◽  
◽  
Salim Latif Mohammed ◽  
Sadik Kamel Gharghan
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based

A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band

2020 ◽  
pp. 1-10
Author(s):  
Abdullah Genc
Keyword(s):  
Path Loss ◽  
Forest Area ◽  
Occupancy Rate ◽  
Loss Model ◽  
Path Loss Model ◽  
Model Based ◽  
Proposed Model ◽  
Pine Trees ◽  
Foliage Density ◽  
Reference Measurements

Abstract In this paper, a new empirical path loss model based on frequency, distance, and volumetric occupancy rate is generated at the 3.5 and 4.2 GHz in the scope of 5G frequency bands. This study aims to determine the effect of the volumetric occupancy rate on path loss depending on the foliage density of the trees in the pine forest area. Using 4.2 GHz and the effect of the volumetric occupancy rate contributes to the literature in terms of novelty. Both the reference measurements to generate a model and verification measurements to verify the proposed models are conducted in three different regions of the forest area with double ridged horn antennas. These regions of the artificial forest area consist of regularly sorted and identical pine trees. Root mean square error (RMSE) and R-squared values are calculated to evaluate the performance of the proposed model. For 3.5 and 4.2 GHz, while the RMSEs are 3.983 and 3.883, the values of R-squared are 0.967 and 0.963, respectively. Additionally, the results are compared with four path loss models which are commonly used in the forest area. The proposed one has the best performance among the other models with values 3.98 and 3.88 dB for 3.5 and 4.2 GHz.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

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