Measurement Selection Strategies for Position Estimation in Indoor Environments

TL;DR

This paper proposes measurement selection strategies using ray-tracing simulations to improve indoor position estimation accuracy in NLoS conditions, demonstrating their effectiveness through experiments.

Contribution

It introduces novel measurement selection strategies based on AP neighborhood information to enhance indoor positioning accuracy under NLoS conditions.

Findings

Measurement selection strategies improve position accuracy in NLoS environments.

Ray-tracing simulations effectively characterize propagation environments.

AP neighborhood-based methods outperform fixed measurement approaches.

Abstract

Time-based indoor positioning techniques rely on multiple access points (APs) and measurements between the user equipment (UE) and the APs. In dense indoor environments, occlusion-induced non-line-of-sight (NLoS) propagation introduces significant delays in these measurements, thereby degrading position estimation accuracy. To address this challenge, this paper proposes measurement selection strategies to improve position estimation accuracy. A ray-tracing (RT) simulator is employed to characterize the propagation environment and derive AP neighborhood information, which is subsequently used to design and evaluate different measurement selection strategies. The approaches explored include AP neighborhood-based cardinality selection, intersection and union of measurements from AP neighborhoods, and fixed measurement selection. Experiments demonstrate the efficacy of the proposed measurement selection strategies in environments under significant NLoS conditions.