Measurement Selection Strategies for Position Estimation in Indoor Environments
Addresses the problem of degraded position estimation accuracy due to NLoS delays in indoor environments for localization systems.
Proposed measurement selection strategies using ray-tracing-derived AP neighborhood information to improve position estimation accuracy in dense indoor environments with NLoS propagation; experiments show efficacy under significant NLoS conditions.
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.