Mean Localization Error vs. SNR for Signal Processing Methods

A benchmark dataset comparing mean localization error in kilometers against Signal-to-Noise Ratio for multiple methods, including new baselines CW-Geo, Unroll, Trans-Loc, and Yu-adapted. The data was authored by Hongmin Wang and last updated on June 2, 2026. Values are means with standard deviations across five evaluation seeds typically between 0.05 and 0.10 km.

Use Cases

• Benchmarking new localization algorithms based on the provided mean error and SNR metrics.
• Comparing the performance of established methods against the new CW-Geo, Unroll, Trans-Loc, and Yu-adapted baselines.
• Analyzing the relationship between localization error and signal quality based on the SNR values.
• Validating theoretical bounds by comparing method performance against the FIM-derived Cramér–Rao lower bound (CRLB).

Strengths

• Includes a theoretical performance bound (FIM-derived CRLB) for validation.
• Reports standard deviations (0.05–0.10 km) across five evaluation seeds, indicating result stability.
• Highlights four new baseline methods (CW-Geo, Unroll, Trans-Loc, Yu-adapted) for contemporary comparison.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Likely contains results from computational experiments or simulations evaluating localization methods.

Freshness

Last updated 2026-06-02 17:36:02; freshness should be verified.