Evaluation of different signal propagation models for a mixed indoor-outdoor scenario using empirical data

In this paper, we are choosing a suitable indoor-outdoor propagation model out of the existing models by considering path loss and distance as parameters. A path loss is calculated empirically by placing emitter nodes inside a building. A receiver placed outdoors is represented by a Quadrocopter (QC) that receives beacon messages from indoor nodes. As per our analysis, the International Telecommunication Union (ITU) model, Stanford University Interim (SUI) model, COST-231 Hata model, Green-Obaidat model, Free Space model, Log-Distance Path Loss model and Electronic Communication Committee 33 (ECC-33) models are chosen and evaluated using empirical data collected in a real environment. The aim is to determine if the analytically chosen models fit our scenario by estimating the minimal standard deviation from the empirical data

Effect of Path Loss Propagation Model on the Position Estimation Accuracy of a 3-Dimensional Minimum Configuration Multilateration System

The 3-Dimensional (3-D) position estimation (PE) accuracy of a multilateration (MLAT) system depends on several factors one of which is the accuracy at which the time difference of arrival (TDOA) measurements are obtained. In this paper, signal attenuation is considered the major contributor to the TDOA estimation error and the effect of the signal attenuation based on path loss propagation model on the PE accuracy of the MLAT system is determined. The two path loss propagation models are considered namely: Okumura-Hata and the free space path loss (FSPL) model. The transmitter and receiver parameters used for the analysis are based on actual system used in the civil aviation. Monte Carlo simulation result based on square ground receiving station (GRS) configuration and at selected aircraft positions shows that the MLAT system with the Okumura-Hata model has the highest PE error. The horizontal coordinate and altitude error obtained with the Okumura-Hata are 2.5 km and 0.6 km respectively higher than that obtained with the FSPL mode

Empirical Valuation of Multi-Parameters and RMSE-Based Tuning Approaches for the Basic and Extended Stanford University Interim (SUI) Propagation Models

In this paper, the prediction performance evaluation of Stanford University Interim (SUI) Model and the extended SUI model are presented. More importantly, the effectiveness of two model tuning approaches, namely, RMSE-based tuning and multi-parameter tuning are assessed based on empirical pathloss data obtained for a suburban area in Uyo, Akwa Ibom state.  Although the RMSE tuning is quite simple, the results showed that in some cases it does not minimize the prediction error to an acceptable level (6dB to 7dB) for practical applications. However, in the two models, the multi-parameter tuning effectively minimized the prediction error to an acceptable level with mean prediction error of about 0.00001 dB, RMSE that are less than 2.45 dB and prediction accuracies above 98.2%. On the other hand, the RMSE-tuned models have mean prediction error of above ± 1.5 dB, RMSE that above 8.8 dB and prediction accuracies less than 94.3%. In all, the SUI model performed better than the extended SUI

Empirical Valuation of Multi-Parameters and RMSE-Based Tuning Approaches for the Basic and Extended Stanford University Interim (SUI) Propagation Models

In this paper, the prediction performance evaluation of Stanford University Interim (SUI) Model and the extended SUI model are presented. More importantly, the effectiveness of two model tuning approaches, namely, RMSE-based tuning and multi-parameter tuning are assessed based on empirical pathloss data obtained for a suburban area in Uyo, Akwa Ibom state.  Although the RMSE tuning is quite simple, the results showed that in some cases it does not minimize the prediction error to an acceptable level (6dB to 7dB) for practical applications. However, in the two models, the multi-parameter tuning effectively minimized the prediction error to an acceptable level with mean prediction error of about 0.00001 dB, RMSE that are less than 2.45 dB and prediction accuracies above 98.2%. On the other hand, the RMSE-tuned models have mean prediction error of above ± 1.5 dB, RMSE that above 8.8 dB and prediction accuracies less than 94.3%. In all, the SUI model performed better than the extended SUI