Wi-Fi-Based Indoor Localization With Interval Random Analysis and Improved Particle Swarm Optimization

The rise of the Internet of Things has spurred the growth of wireless applications, particularly Wi-Fi-based indoor localization, which is gaining prominence owing to its cost-effectiveness. Nevertheless, the accuracy of Wi-Fi-based indoor localization is hindered by signal instability. To address this limitation, we introduce an interval random analysis approach for uncertain Wi-Fi-based indoor localization. Specifically, this approach employs an interval random parameter lognormal shadowing model for radio map enhancement and adaptive Bayesian comprehensive learning (IRPLS-ABCL) particle swarm optimization (PSO) for location estimation accuracy enhancement. The process comprises two stages: offline training and online localization. During the offline phase, we establish the interval random parameter lognormal shadowing model, considering the parameters as interval random variables, rather than precise values, in a sparse reference point scenario. In the online phase, we use a double-panel fingerprint homogeneity model to assess fingerprint similarity and apply the adaptive Bayesian comprehensive learning PSO algorithm to enhance localization precision. The experimental results show that the proposed algorithm can achieve the best performance in terms of localization accuracy based on the predicted average received signal strength (RSS), reaching 1.89 m.