# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 3 System Overview
### SPSO Algorithm for Localization
B Impact of different distance metricsWi-Fi fingerprint-based indoor localization is inseparable from the comparison of similar fingerprints. It is clear that different distance-characterization methods will lead to different localization results with the two-panel fingerprint-homogeneity model. A comparative experiment was conducted to analyze their impact on localization performance. The reliable covariance matrix cannot be obtained from the sparse training set; we can see in Table 3 that the accuracy of the combination involving Mahalanobis distance performs poorly. The two-panel method using Euclidean metric and cosine distance achieved the best performance. This is why we used them for Wi-Fi fingerprint-similarity characterization in this study. C Impact of different weight assignmentsIn fact, the neighboring fingerprints can generally be found correctly through different distance metrics. However, achieving high-accuracy localization with proper weight assignment is a challenging problem, because the transformation relationship from the fingerprint domain to the physical coordinate domain is uncertain. The effects of three weight assignments (Equation (26)) were compared experimentally in this study. Obviously, weight 1 is often applied, but it is not always the best. Weight 2 and weight 3 have little difference in actual performance. For the proposed method, weight 3 performs best with the two-panel approach using Euclidean metric and cosine distance. It verified the viability of weight 3 in Wi-Fi fingerprint-based indoor localization. ## 5 Conclusions

Although indoor localization based on Wi-Fi is promising, achieving improved accuracy remains a difficult problem. In this study, an application method of a particle swarm optimization algorithm in Wi-Fi fingerprint location was proposed, which adopted a two-panel fingerprint-homogeneity model to express the similarity among different fingerprints with greater robustness. The experimental results showed that the average accuracy of the proposed localization system was 2.0817 m. Further, the proposed particle swarm optimization algorithm outperforms other conventional algorithms, verifying its effectiveness and feasibility for improving the accuracy of indoor localization. Future work will focus on extending radio fingerprint maps and mitigating the effects of Wi-Fi signal volatility. In addition, fusion localization with other methods will be considered to improve the performance of the localization system by combining the advantages offered by the different sensors. All authors conceptualized the study and participated in the development of the concept and methodology; software, K.L. and X.Z. ; validation, K.L. and X.Z. ; formal analysis, X.Z. ; investigation, K.L. and X.Z. ; resources, J.Z. ; data curation, K.L. ; writing--original draft preparation, K.L. ; writing--review and editing, K.L. and X.Z. ; visualization, K.L. and X.Z. ; supervision, J.Z. ; project administration, J.Z. ; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: