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

TL;DR

This paper introduces a survey-free method for radio map construction using unlabeled RSS data and an HMM-based coarse-to-fine inference framework, eliminating the need for manual site surveys.

Contribution

It presents a novel HMM-based approach that aligns unlabeled RSS sequences with physical regions and locations, enabling accurate radio map creation without surveys.

Findings

Achieves a radio map MAE of 8.96 dB in an office environment.

KNN localization based on the estimated map has an average error of 3.33 meters.

Demonstrates the viability of survey-free radio map construction in corridor-guided environments.

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.