Survey-Free Radio Map Construction via HMM-Based Coarse-to-Fine Inference

2026-05-10

AI Summary

This work proposes a fully automated method for constructing wireless signal maps without manual site surveys or labeled data. Leveraging only unlabeled received signal strength (RSS) sequences and a known indoor floorplan, the approach integrates hidden Markov models (HMMs), graph-structured inference, and RSS propagation characteristics to perform coarse-to-fine spatial alignment and region labeling. Notably, it is the first method to achieve signal map construction under the sequence topology assumption without auxiliary sensors or human annotations. Experimental results in an office environment demonstrate a mean absolute error of 8.96 dB in the constructed signal map, and when used for KNN-based localization, the map yields an average positioning error of 3.33 meters.

Abstract

Traditional radio map construction methods mandate labor-intensive data collection and precise location labeling. To address these limitations, we propose a novel survey-free approach for radio map construction that relies solely on unlabeled Received Signal Strength (RSS) measurements, thereby obviating the need for manual site surveys or auxiliary Inertial Measurement Units (IMUs). The key idea involves embedding multiple unlabeled RSS sequences into a known indoor layout, specifically targeting corridor-guided environments with a dominant unidirectional pedestrian flow. However, aligning the embedded coordinates with the RSS collection locations remains challenging due to the random fluctuations inherent in RSS data. To tackle this, we introduce a Hidden Markov Model (HMM)- based Coarse-to-Fine Inference (HCFI) framework. At the coarse level, we employ an HMM-based region label inference algorithm to partition RSS sequences and align the RSS segments with specific physical regions using graph-based inference. At the fine level, we develop an HMM-based location label inference technique to estimate RSS collection coordinates by leveraging RSS propagation principles while incorporating sequential spatio-temporal mobility probability. Empirical results from an office environment demonstrate that the proposed method achieves a radio map construction Mean Absolute Error (MAE) of 8.96 dB. Furthermore, based on the estimated radio map, k-Nearest Neighbor (KNN) localization yields an average positioning error of approximately 3.33 meters, offering a highly viable, survey-free solution for radio map construction under sequential topological assumptions.

Problem

• radio map construction
• survey-free
• Received Signal Strength
• location labeling
• indoor localization

Innovation

• survey-free
• radio map construction
• Hidden Markov Model (HMM)
• coarse-to-fine inference
• RSS sequence alignment