A hierarchical positioning model for WiFi-based indoor localization in large-scale complex environments

In developing Wi-Fi indoor positioning systems for large-scale complex environments, the fundamental challenge lies in the significant impact of signal noise on high-frequency data volatility, which substantially degrades positioning accuracy. To address this limitation, we propose an improved hierarchical positioning model combining a Gaussian mixture model (GMM) regional classifier with random forest secondary classifiers. During the offline phase, recognizing that Wi-Fi signal strength typically follows Gaussian distributions, we employed GMM to partition the target area into non-overlapping sub-regions with similar signal strength characteristics. For each sub-region, we then trained dedicated random forest classifiers. In the online phase, the system first identifies the probable sub-region using the GMM classifier before applying the corresponding random forest classifier for precise location estimation. We evaluated our approach in an indoor parking lot featuring an irregular layout, numerous solid walls, scattered access point distribution, and intermittent electromagnetic interference. Experimental results demonstrated that our hierarchical model delivers satisfactory performance for indoor location-based services in such challenging large-scale environments.