# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Experiment with Proximity-Based Adaptive Algorithm
#### 4.3.2 Experiment Evaluation in Static State  
For this section, an experiment is conducted to assess and compare the efficacy of the proposed method under stationary conditions. The positioning points are shown in  
Figure 12: Experimental environment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Threshold (m)** & **2.0** & **3.0** & **4.0** & **5.0** \\ \hline Side position & 1.19 & 0.96 & 0.97 & 0.97 \\ Center position & 3.26 & 3.35 & 4.22 & 4.79 \\ Other position & 5.07 & 4.83 & 4.99 & 5.21 \\ Average & 3.17 & 3.05 & 3.39 & 3.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Distance MAE according to threshold \(T_{p}\). Figure 12. Positioning is iterated 1000 times at each of the 20 stationary points. For performance evaluation, we compare various filtering combinations. Three filtering combinations are considered: OR, OR + KF, and OR + KF + LOR + MAF. Each combination is tested with and without the application of the MP technique. The proposed method is the PAP algorithm, which means OR + KF + LOR + MAF filtering and location estimation using trilateration and MP techniques depends on proximity. The threshold \(T_{p}\) for identifying proximity status in the PAP algorithm is set at 3 m. Table 6 presents the average Euclidean distance between the estimated and actual position coordinates as a result of performance comparison under stationary conditions, contingent upon the application of the filtering algorithm and MP. In Table 6, 'O' means that the corresponding technique or filtering is applied to the positioning method. Methods using the MP technique show overall improved performance compared to methods using only trilateration. In particular, the MP technique reduced positioning errors for side positions located within 1 m of the AP. Other positions also exhibited lower positioning errors when the MP technique was applied. For center positions, where occurrences of exceeding \(T_{p}\), indicating proximity to the AP, are almost nonexistent, applying KF would result in the same outcome regardless of whether MP is applied. However, in methods where KF is not applied to RSSI, intermittent occurrences of exceeding \(T_{p}\) are observed due to the unstable RSSI being directly applied, prompting the application of MP. The static state experiment results are obtained by calculating 1000 consecutive position estimation coordinates for each method and the actual physical receiver position coordinates using the Euclidean distance formula, and the unit is meters (m). For the experimental results, the average positioning error of the method using raw RSSI and only the trilateration technique is about 4.14 m, and the average positioning error of the method using only the trilateration technique by applying OR + KF + LOR + MAF filtering is about 4.14 m. Since it is 3.29 m, the positioning accuracy is reduced by about 21%. In addition, the result of positioning using the PAP algorithm is that OR + KR + LOR + MAF filtering is applied, and trilateration and MP technique positioning are included. The average positioning error is about 2.38 m, so OR + KF + LOR + MAF filtering is applied, and the position error is reduced by about 28% compared to the positioning error determined using only trilateration. The dynamic positioning test evaluates the performance of the PAP algorithm by comparing the performance of the proposed method with a positioning technique using only trilateration with the same filtering algorithm applied. For the experiment, location estimation is conducted while moving at a constant speed along a designated path. Figure 14 shows the location estimation results of the experiment along the positioning path. In Figure 14, the black solid line is the actual path, the red solid line represents the location estimation path using only trilateration, and the blue solid line represents the location estimation path through the PAP algorithm. Figure 14 shows the experimental results when walking in a straight line through the middle of APs.