The diversity of technologies and approaches in Wireless Positioning Systems (WPS) necessitates a structured taxonomy to better understand their capabilities and limitations. Recent advancements in Artificial Intelligence (AI) have significantly enhanced the accuracy and efficiency of WPS by leveraging sophisticated frameworks. The traditional reliance on signal-based metrics, such as received signal strength (RSS), faces significant challenges in adapting to environmental dynamics and mitigating inaccuracies, highlighting the critical role of Artificial Intelligence (AI) in developing more intelligent and adaptive frameworks within Wireless Positioning Systems (WPS). This paper aims to identify and analyze the potential of AI-enhanced WPS frameworks in improving accuracy and robustness, with a specific focus on Wi-Fi and RSS-based methods such as signal fingerprinting technique. The methods taken to characterize the taxonomy include Systematic Literature Review (SLR) and Bibliometric Analysis to identify, categorize, and analyze WPS frameworks that leverage AI to process RSS data and improve position estimation. This study provides a structured taxonomy and highlights the transfor
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