## 4 Experiment and Analysis
### Experimental Process  
In the offline stage, the surveyor carries an Honor 50 mobile phone and collects fingerprints at the experimental location in order to construct the WiFi fingerprint reference database. The surveyor collects the first point at the starting point and then collects a point every 3-10 m stay at each point for 5 s. The path is along the predetermined path to collect data. Once the data are collected, they are imported into the database for further processing. In the online stage, the surveyor carries a Nexus 5X to collect the data including acceleration, angular velocity, and WiFi data. Smartphone modes during the positioning procedure include calling, dangling, handheld, and pocketed [34]. The scenarios and actual trajectories of the two experiments are shown in Figure 9 below. The parameters of the experiments are shown in Table 1. The diagram illustrating the four smartphone motions is shown in Figure 10[41]. The components of the mobile phone are shown in Table 2 below. Figure 11 illustrates the impact of the AO algorithm in AO-PF, highlighting the differences in particle distribution and weights before and after applying AO. In Figure 11a, before AO optimization at step k = 100, the particles are concentrated in the center but have scattered, uneven weights. Many particles have low weights, contributing minimally to the posterior distribution, which can reduce accuracy and lead to particle impoverishment in complex or nonlinear navigation environments. In contrast, Figure 11b shows that after optimization by the AO algorithm, it is evident that as the number of low-weight particles decreases, the total number of particles remains unchanged, leading to an increase in the number of high-weight particles. In particle filtering, higher weights increase the likelihood of particles being selected in subsequent iterations, which in turn increases the  
Figure 8: This diagram illustrates the effect of introducing AO particles in the AO-PF framework. The introduction of AO particles aims to address the particle impoverishment problem. Compared to the standard particle filter [39], the offspring particles obtained after resampling cover more areas of the true posterior distribution.  
value of the particles [42]. This demonstrates that the AO algorithm enhances location estimation accuracy by adjusting particle weights and increasing the proportion of high-quality particles.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Particle size of \(PSO\) & 50 \\ \(\omega\) & 0.01 \\ \(c1\) & 0.01 \\ \(c2\) & 0.01 \\ Particle size of \(PF\) & 500 \\ \(N\) (particle size of \(AO\)) & 500 \\ \(D\) & 2 \\ \(lb\) & \(-1\) \\ \(ub\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of the experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phone** & **Honor 50** & **Nexus 5X** \\ \hline WiFi chip model & FastConnect 6200 & BCM4358 \\ Accelerometer & BMA456 & BMA255 \\ Gyroscope & LSM6 DSO & MPU-6500 \\ Magnetometer & AK09918C & AK8963 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Phone components.  
Figure 10: The diagram of the four smartphone motions.  
Figure 9: Scenarios and real trajectory of the two experiments.