Channel Knowledge Map-Enabled NLoS ISAC Localization

TL;DR

This paper introduces a channel knowledge map-based localization framework that improves accuracy in NLoS environments by leveraging environment-aware path signatures and geometric priors, outperforming fingerprinting methods.

Contribution

The novel environment-aware localization framework utilizes CKM to learn AoA-ToA signatures and jointly estimate user and scatterer locations, enhancing NLoS localization robustness.

Findings

Outperforms fingerprinting in NLoS scenarios

Provides robust and scalable localization in complex environments

Utilizes CKM to correct for imperfect CSI matching

Abstract

Accurate localization in non-line-of-sight (NLoS) environments remains challenging even with both angle-of-arrival (AoA) and time-of-arrival (ToA) measurements. In complex urban scenarios, the absence of line-of-sight (LoS) paths and the lack of environment prior knowledge make geometric based localization methods inapplicable, while prior-based approach such as fingerprinting is sensitive to environmental perturbations. This paper proposes a novel environment-aware localization framework enabled by the emerging concept called channel knowledge map (CKM). In the offline stage, AoA-ToA path signatures are learned by the CKM, with each path mapped to one candidate scatterer, thereby forming geometric priors within the environment. In the online stage, observed paths are matched to the CKM to extract high-confidence scatterers. Nonlinear least squares (NLS) method is then applied to jointly estimate the user and dominant scatterer locations. Even with imperfect CSI matching, geometric feasibility consistent with CKM scatterer priors provides corrective information and suppresses ambiguity. Simulations demonstrate that the proposed scheme outperforms fingerprinting and offers a robust and scalable solution to address the challenging NLoS localization for integrated sensing and communication (ISAC) systems.