UNILocPro: Unified Localization Integrating Model-Based Geometry and Channel Charting

2025-10-31

AI Summary

To address low localization accuracy and poor generalization in hybrid line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios, this paper proposes UNILocPro—a unified framework that jointly leverages geometric model–driven localization and channel chart (CC)–based data-driven learning via LoS/NLoS adaptive identification. It introduces an optimal transport–based similarity metric and a multi-task loss function incorporating pairwise distance, triplet, LoS-aware, and global structural preservation terms, enabling unsupervised training through a pretraining–pseudo-labeling strategy. Furthermore, we propose UNILoc—a low-complexity variant that eliminates iterative optimization. Experiments demonstrate that UNILocPro achieves performance on par with fully supervised fingerprinting methods under completely unsupervised settings, while UNILoc retains nearly identical accuracy with significantly reduced computational overhead.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In this paper, we propose a unified localization framework (called UNILocPro) that integrates model-based localization and channel charting (CC) for mixed line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios. Specifically, based on LoS/NLoS identification, an adaptive activation between the model-based and CC-based methods is conducted. Aiming for unsupervised learning, information obtained from the model-based method is utilized to train the CC model, where a pairwise distance loss (involving a new dissimilarity metric design), a triplet loss (if timestamps are available), a LoS-based loss, and an optimal transport (OT)-based loss are jointly employed such that the global geometry can be well preserved. To reduce the training complexity of UNILocPro, we propose a low-complexity implementation (called UNILoc), where the CC model is trained with self-generated labels produced by a single pre-training OT transformation, which avoids iterative Sinkhorn updates involved in the OT-based loss computation. Extensive numerical experiments demonstrate that the proposed unified frameworks achieve significantly improved positioning accuracy compared to both model-based and CC-based methods. Notably, UNILocPro with timestamps attains performance on par with fully-supervised fingerprinting despite operating without labelled training data. It is also shown that the low-complexity UNILoc can substantially reduce training complexity with only marginal performance degradation.

Problem

• Integrates model-based and channel charting localization for mixed LoS/NLoS scenarios
• Achieves unsupervised learning by preserving global geometry with multiple loss functions
• Reduces training complexity via low-complexity implementation with minimal performance degradation

Innovation

• Unified framework integrates model-based and channel charting methods
• Adaptive activation based on LoS and NLoS scenario identification
• Low-complexity implementation reduces training with minimal performance loss