NLoS Localization with Single Base Station Based on Radio Map

TL;DR

The paper tackles outdoor NLoS localization with a single base station by integrating sequential signal measurements with a pre-constructed radio map. It formalizes a probabilistic framework and two operational regimes (known vs unknown relative displacements), proposing a trajectory-matching approach that leverages RM predictions to mitigate multipath effects. Through extensive simulations on the RadioLocSeer dataset, it demonstrates sub-meter accuracy across various RM construction strategies and sequence lengths, with higher mobility and well-matched RMs yielding the best performance. The work highlights practical benefits for scalable single-BS deployments and points to data-driven RM adaptation and real-world validation as future directions.

Abstract

Accurate outdoor localization in Non-Line-of-Sight (NLoS) environments remains a critical challenge for wireless communication and sensing systems. Existing methods, including positioning based on the Global Navigation Satellite System (GNSS) and triple Base Stations (BSs) techniques, cannot provide reliable performance under NLoS conditions, particularly in dense urban areas with strong multipath effects. To address this limitation, we propose a single BS localization framework that integrates sequential signal measurements with prior radio information embedded in the Radio Map (RM). Using temporal measurement features and matching them with radio maps, the proposed method effectively mitigates the adverse impact of multipath propagation and reduces the dependence on LoS paths. Simulation experiments further evaluate the impact of different radio map construction strategies and the varying lengths of the measurement sequence on localization accuracy. Results demonstrate that the proposed scheme achieves sub-meter positioning accuracy in typical NLoS environments, highlighting its potential as a practical and robust solution for single-base-station deployment.

Figures (3)

• Figure 1: Comparison of trilateration-based positioning method (left) and our proposed RM-based positioning method (right). The left subfigure illustrates the conventional geometry-based trilateration method, where the target position is estimated using measurements (such as ToA, TDoA, AoA) from multiple BSs. However, under NLOS conditions, signal blockage and multipath effects lead to large positioning errors, as shown by the deviation between the true and estimated positions. In contrast, the right subfigure demonstrates our proposed method, which achieves accurate localization even in NLOS environments using signals from a single base station by exploiting the spatial characteristics of the radio map.
• Figure 2: Workflow of our proposed positioning method. The overall framework consists of two stages. In the first stage, the RM is constructed either through sampling-based approaches (e.g., interpolation or tensor completion) or non-sampling-based approaches (e.g., generative modeling). In the second stage (our main focus in this work), the user performs online localization by combining the constructed RM with its own temporal signal measurements, effectively inferring its position even under complex propagation conditions.
• Figure 3: We combine the RM of IRT4 with the corresponding IRT4 measurement sequences to evaluate positioning accuracy under different sequence lengths, which serves as the baseline scenario. In this case, the constructed RM perfectly matches the ground-truth radio environment, representing an ideal condition; in practice, however, inevitable noise and modeling errors exist. The speed of the device in this set of visualized images is 5 m/s. The yellow lines represent the sequence paths, the green dots denote the start points of the sequences, the blue dots indicate the end points (i.e., the true current positions), and the red crosses mark the predicted positions.