# AI-Driven Decimeter-Level Indoor Localization Using Single-Link Wi-Fi: Adaptive Clustering and Probabilistic Multipath Mitigation

## TL;DR

This paper introduces an AI-based system for precise indoor positioning using single-link Wi-Fi, achieving decimeter-level accuracy without complex infrastructure.

## Contribution

The novel framework uses adaptive clustering and probabilistic methods to suppress multipath interference in NLOS environments with single-link Wi-Fi.

## Key findings

- The proposed system achieves an average localization error of 0.63 m on the Widar2.0 dataset.
- It outperforms existing methods like Widar2.0 and Dynamic-MUSIC in accuracy and efficiency.
- The system is compatible with commodity Wi-Fi hardware and suitable for real-time human tracking.

## Abstract

What are the main findings?
An AI-driven single-link Wi-Fi CSI localization framework is proposed, achieving decimeter-level indoor positioning accuracy without relying on historical trajectories or multi-link infrastructure.A two-stage TOF estimation combined with adaptive spatio-temporal AOA clustering effectively suppresses multipath interference and eliminates cumulative localization errors in NLOS environments.

An AI-driven single-link Wi-Fi CSI localization framework is proposed, achieving decimeter-level indoor positioning accuracy without relying on historical trajectories or multi-link infrastructure.

A two-stage TOF estimation combined with adaptive spatio-temporal AOA clustering effectively suppresses multipath interference and eliminates cumulative localization errors in NLOS environments.

What are the implications of the main findings?
The proposed approach enables low-cost, real-time indoor localization using commodity Wi-Fi hardware, making it suitable for large-scale deployment in smart buildings and human tracking applications.The single-link, training-free architecture provides a robust alternative to fingerprinting and multi-link systems, offering improved adaptability to dynamic and complex indoor environments.

The proposed approach enables low-cost, real-time indoor localization using commodity Wi-Fi hardware, making it suitable for large-scale deployment in smart buildings and human tracking applications.

The single-link, training-free architecture provides a robust alternative to fingerprinting and multi-link systems, offering improved adaptability to dynamic and complex indoor environments.

This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised intelligence modules into the localization pipeline. A refined two-stage time-of-flight (TOF) estimation method is introduced, combining a minimum-norm algorithm with a probability-weighted refinement mechanism that improves ranging accuracy under non-line-of-sight (NLOS) conditions. Simultaneously, an adaptive parameter-tuned DBSCAN algorithm is applied to angle-of-arrival (AOA) sequences, enabling unsupervised spatio-temporal clustering for stable direction estimation without requiring prior labels or environmental calibration. These AI-enabled components allow the system to dynamically suppress multipath interference, eliminate positioning ambiguity, and maintain robustness across diverse indoor layouts. Comprehensive experiments conducted on the Widar2.0 dataset demonstrate that the proposed method achieves decimeter-level accuracy with an average localization error of 0.63 m, outperforming existing methods such as “Widar2.0” and “Dynamic-MUSIC” in both accuracy and efficiency. This intelligent and lightweight architecture is fully compatible with commodity Wi-Fi hardware and offers significant potential for real-time human tracking, smart building navigation, and other location-aware AI applications.