Multi-Surrogate-Teacher Assistance for Representation Alignment in Fingerprint-based Indoor Localization

TL;DR

This paper introduces a plug-and-play framework with surrogate teachers to align representations across diverse RSS fingerprint datasets, improving indoor localization accuracy by reducing environment-sensitive biases.

Contribution

The work proposes a novel two-phase knowledge transfer framework using surrogate generative teachers to enhance transferable representations in specialized localization networks.

Findings

Significant improvement in localization accuracy on benchmark datasets.

Effective reduction of environment-sensitive biases in RSS datasets.

Framework demonstrates strong generalization across different indoor environments.

Abstract

Despite remarkable progress in knowledge transfer across visual and textual domains, extending these achievements to indoor localization, particularly for learning transferable representations among Received Signal Strength (RSS) fingerprint datasets, remains a challenge. This is due to inherent discrepancies among these RSS datasets, largely including variations in building structure, the input number and disposition of WiFi anchors. Accordingly, specialized networks, which were deprived of the ability to discern transferable representations, readily incorporate environment-sensitive clues into the learning process, hence limiting their potential when applied to specific RSS datasets. In this work, we propose a plug-and-play (PnP) framework of knowledge transfer, facilitating the exploitation of transferable representations for specialized networks directly on target RSS datasets through two main phases. Initially, we design an Expert Training phase, which features multiple surrogate generative teachers, all serving as a global adapter that homogenizes the input disparities among independent source RSS datasets while preserving their unique characteristics. In a subsequent Expert Distilling phase, we continue introducing a triplet of underlying constraints that requires minimizing the differences in essential knowledge between the specialized network and surrogate teachers through refining its representation learning on the target dataset. This process implicitly fosters a representational alignment in such a way that is less sensitive to specific environmental dynamics. Extensive experiments conducted on three benchmark WiFi RSS fingerprint datasets underscore the effectiveness of the framework that significantly exerts the full potential of specialized networks in localization.