## 5 Analysis of the Results
### Body Shadowing Component  
According to Equation (1), one of the key elements of the system loss is represented by the component associated with the body shadowing \(\Delta L_{BS}\). Due to the random nature of human motion, \(\Delta L_{BS}\) is also random, thus, it is represented by a random variable. Obtaining the \(\Delta L_{BS}\) component is possible by a low-pass filtration of the empirical data. The method of this filtration depends on the analyzed measurement scenario. In static scenarios, the empirical data are averaged over time, yielding the composite component \(\overline{L_{SL}(d)}\), i.e. :  
\[\overline{L_{SL}(d)}=\overline{L_{S}(d)}+\Delta L_{BS}=\frac{1}{N_{t}}\sum_{i =0}^{N_{t}}L_{SL}(d,i), \tag{3}\]  
where \(N_{t}\) is the number of received signal samples obtained within a certain time window for the analyzed distance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MN LOCATION**} & \multirow{2}{*}{**SCENARIO**} & \multirow{2}{*}{**LOS/NLOS Conditions**} & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**UWB**} \\ \cline{3-6}  & & & \(n\) & \(\overline{L_{S}(d_{0})}_{(\text{dB})}\) & \(n\) & \(\overline{L_{S}(d_{0})}_{(\text{dB})}\) \\ \hline \multirow{3}{*}{\(\text{HE}_{\text{R}}\)} & \multirow{3}{*}{APR} & LOS & 0.32 & 75.9 & 0.61 & 23.4 \\  & & NLOS & 2.56 & 72.1 & 3.46 & 26.1 \\ \cline{2-6}  & & \multirow{2}{*}{\(\text{DEP}\)} & LOS & 0.34 & 71.9 & 0.79 & 25.7 \\  & & NLOS & 0.16 & 76.2 & 3.12 & 27.7 \\ \hline \multirow{3}{*}{\(\text{TO}_{\text{F}}\)} & \multirow{3}{*}{APR} & LOS & 0.24 & 64.7 & 0.13 & 24.0 \\  & & NLOS & 2.51 & 65.9 & 2.41 & 27.3 \\ \cline{2-6}  & & \multirow{2}{*}{\(\text{DEP}\)} & LOS & 0.34 & 73.3 & 0.85 & 28.4 \\  & & NLOS & 1.40 & 73.6 & 2.16 & 29.5 \\ \hline \multirow{3}{*}{\(\text{AB}_{\text{L}}\)} & \multirow{3}{*}{APR} & LOS & 0.16 & 68.3 & 0.54 & 25.6 \\  & & NLOS & 3.80 & 69.7 & 1.25 & 30.7 \\ \cline{1-1} \cline{2-6}  & & \multirow{2}{*}{\(\text{DEP}\)} & LOS & 0.21 & 70.8 & 0.27 & 28.9 \\ \cline{1-1}  & & NLOS & 2.95 & 71.9 & 2.58 & 28.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Obtained values of SLE and MSL at reference distance. In dynamic scenarios, the filtration of the empirical data cannot be performed directly by using Equation (3) and requires spatial filtering due to the variable distance between the measurement nodes. Such filtration was realized with a linear moving average filter, which can be described by the following expression:  
\[\overline{L_{SL}(d)}=\frac{1}{M}\sum_{j=0}^{M-1}L_{SL}[k-j],\ k=M,\ M+1,\ M+2, \ldots N_{t}, \tag{4}\]  
where \(M\) is the length of the averaging window, which includes the number of samples corresponding to ten wavelengths (\(10\lambda\)). This is the selected value of the spatial filter resolution, determined from measurements [27]. It should be mentioned that the user's motion velocity was varying during the measurements, thus, the length of the filter was adjusted. These factors determined the development of the adaptive filter, whose length is selected on the basis of the estimated parameters of the user's motion during the measurements. The body shadowing component is obtained by subtracting the MSL (Equation (2)) from the composite component (4), i.e. :  
\[\Delta L_{BS}=\overline{L_{SL}(d)}-\overline{L_{S}(d)}. \tag{5}\]  
In order to model the large-scale fading component, the best fitting probability distribution was chosen. Due to the physics of the underlying phenomena, the losses affecting the received signal add up in the logarithmic domain. This yields a lognormal distribution of the large-scale fading component, as confirmed by the numerous studies available in the literature [37]. The lognormal probability distribution was fitted to the empirical data in linear domain (\(\Delta L_{BS}\)). This was done in MATLAB, using the _fitdist_ function, which is the implementation of the maximum likelihood estimation.