Localization Accuracy Improvement in Multistatic ISAC with LoS/NLoS Condition using 5G NR Signals

TL;DR

This paper proposes a novel algorithm to enhance localization accuracy in multistatic ISAC systems using 5G NR signals, effectively mitigating outliers and errors in LoS/NLoS conditions, with significant simulation-based performance improvements.

Contribution

It introduces a new method for improving target localization accuracy in multistatic ISAC systems under challenging propagation conditions, leveraging 5G NR PRS signals.

Findings

Achieves up to 28% reduction in average localization error over LS methods.

Attains up to 16% improvement in the 90th percentile of localization error.

Demonstrates effectiveness across urban and indoor environments based on 3GPP standards.

Abstract

Integrated sensing and communication (ISAC) is anticipated to play a crucial role in sixth-generation (6G) mobile communication networks. A significant challenge in ISAC systems is the degradation of localization accuracy due to poor propagation conditions, such as multipath effects and non-line-of-sight (NLoS) scenarios. These conditions result in outlier measurements that can severely impact localization performance. This paper investigates the enhancement of target localization accuracy in multistatic ISAC systems under both line-of-sight (LoS) and NLoS conditions. We leverage positioning reference signal (PRS), which is currently employed in fifth-generation (5G) new radio (NR) for user equipment (UE) positioning, as the sensing signal. We introduce a novel algorithm to improve localization accuracy by mitigating the impact of outliers in range measurements, while also accounting for errors due to PRS range resolution. Eventually, through simulation results, we demonstrate the superiority of the proposed method over previous approaches. Indeed, we achieve up to 28% and 20% improvements in average localization error over least squares (LS) and iteratively reweighted least squares (IRLS) methods, respectively. Additionally, we observe up to 16% and 13% enhancements in the 90th percentile of localization error compared to LS and IRLS, respectively. Our simulation is based on 3rd Generation Partnership Project (3GPP) standards, ensuring the applicability of our results across diverse environments, including urban and indoor areas.