# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 5 Experiment Analysis
### Localization Accuracy  
As the number of impaired APs increases, the stability of the localization system may change. In the actual scenario, the type and intensity of the disaster are random, such as different seismic levels and different intensity of explosion. In order to verify the localization performance of the system after different accidents, this paper carried out 100 localizations at different damage levels and used the average value of the 100 localization results as the localization performance index to evaluate the localization system at the scene of the disaster. As shown in Figure 12, the blue indicates the proposed algorithm, and the red indicates the traditional fingerprint localization algorithm. The abscissa indicates the number of impaired APs, and the ordinate indicates the average localization error. As can be seen from Figure 12, the localization performances of both algorithms decrease as the number of impaired APs increases. When there are impaired APs in the localization environment, the performance of the traditional fingerprinting localization algorithm drops sharply. However, the proposed algorithm can filter the impaired APs better and achieve better localization results. When all the APs are damaged, the algorithm proposed in this paper will have a sharp drop in performance. However, as long as there are normal APs, it can achieve a better localization effect. Therefore, when using the algorithm proposed in this paper, if it judges that all the APs have been impaired, the reference value of the localization result can be reduced during the rescue work. Figure 11: Error analysis of different numbers of impaired APs. (**a**) Error analysis of the proposed algorithm. (**b**) Error analysis of the traditional fingerprinting algorithm. Figure 12: Algorithm positioning performance. Figure 13 shows the superiority of the proposed algorithm. Figure 13 shows the localization accuracy obtained by performing different localization algorithms in the same experimental environment. In this experiment, we first placed an impaired AP in the location environment and then performed the proposed algorithm (SFL), the traditional fingerprinting localization algorithm (FL) and the AP filtering algorithm based on the exclusion method, respectively (FBE). The blue solid line represents the location algorithm proposed in this paper; the black dotted line represents the AP filtering algorithm based on the exclusion method; and the red dotted line represents the traditional fingerprint location algorithm. In Figure 13, the abscissa represents the localization error, and the ordinate represents the probability. In this experiment, 100 locations were located, and then, their localization errors were calculated. Figure 10 shows only the positioning error in a small part of the positioning environment. The result cannot effectively reflect the positioning performance of the algorithm. In order to have a clear error analysis, we calculate the probability distribution of the localization error at intervals of 0.5 m, and the result is shown in Figure 13. There are many factors that affect RSSI in the localization environment, such as movement of people, metal interference, transfer of obstacles and collapse of buildings. The algorithm proposed in this paper can identify the APs whose RSSI is greatly disturbed. However, if the interference level of RSSI is within the threshold \(\Delta\), the algorithm only slightly reduces the reference value of the APs according to the number of votes and cannot completely eliminate APs that have been slightly disturbed. Therefore, after multiple positioning, nearly 10% of the probability will make the localization error 1 m or more.