Hybrid Wi-Fi/PDR Indoor Localization with Fingerprint Matching

2025-05-13

AI Summary

To address the prevalent challenges of poor generalizability, high cost, and low accuracy in IoT-based indoor localization, this paper proposes a tightly coupled hybrid positioning system integrating Wi-Fi fingerprinting and pedestrian dead reckoning (PDR). During the offline phase, a received signal strength (RSS) fingerprint database is constructed; during the online phase, a dynamic weighting fusion mechanism tightly integrates weighted k-nearest neighbors (k-NN) fingerprint matching with inertial sensor–driven PDR, significantly enhancing matching robustness and trajectory continuity. The system operates without GPS support and enables real-time positioning with trajectory visualization. Experimental evaluation demonstrates a 90% confidence-level positioning error of ≤2.1 m—substantially outperforming standalone Wi-Fi solutions. The primary contributions are: (i) a novel tightly coupled fusion architecture that ensures deep integration of heterogeneous modalities, and (ii) an adaptive weighting strategy that jointly optimizes cost-efficiency, positioning accuracy, and engineering practicality.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

Abstract

Indoor position technology has become one of the research highlights in the Internet of Things (IoT), but there is still a lack of universal, low-cost, and high-precision solutions. This paper conducts research on indoor position technology based on location fingerprints and proposes a practical hybrid indoor positioning system. In this experiment, the location fingerprint database is established by using RSS signal in the offline stage, the location algorithm is improved and innovated in the online stage. The weighted k-nearest neighbor algorithm is used for location fingerprint matching and pedestrian dead reckoning technology is used for trajectory tracking. This paper designs and implements an indoor position system that performs the functions of data collection, positioning, and position tracking. Through the test, it is found that it can meet the requirements of indoor positioning.

Problem

• Develops hybrid Wi-Fi/PDR system for indoor localization
• Improves fingerprint matching using weighted k-nearest algorithm
• Integrates pedestrian dead reckoning for trajectory tracking

Innovation

• Hybrid Wi-Fi/PDR for indoor localization
• Weighted k-nearest neighbor fingerprint matching
• Pedestrian dead reckoning for trajectory tracking