Indoor Positioning Experiments with UWB and AoA for NLOS Environments

Experimental data from a study on enhancing indoor positioning accuracy in non-line-of-sight (NLOS) conditions. The dataset, shared by Shu-Hung Lee on figshare, likely contains measurements from tests using ultra-wideband (UWB) and angle of arrival (AoA) technologies. It was last updated in May 2026 and is available under a CC-BY-4.0 license.

Use Cases

• Evaluating the performance of moving average filters for positioning error reduction based on experimental results.
• Testing the adaptive offset cancellation (AOC) method's effectiveness in known target location areas.
• Comparing input membership functions for fuzzy inference systems in area recognition for unknown target locations.
• Benchmarking the combined FAOC method against original positioning algorithms.

Strengths

• Experimental results are quantified with specific error reductions: AOC method reduced errors by an average of 29.38 cm.
• Performance metrics are provided, such as a 95.68% average regional recognition accuracy achieved by one fuzzy inference method.
• The proposed FAOC method is reported to improve average positioning error by 40.9%, from 69.03 cm to 44.48 cm.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (2.7 MB), which may indicate a limited experimental scope.

Provenance

Source

Shu-Hung Lee via figshare

Collection Method

Data obtained from positioning experiments described in an associated research paper.

Freshness

Last updated 2026-05-21 17:25:13; freshness should be verified.