Rf Localization Eval

---
name: rf-localization-eval
description: Evaluates indoor wireless transmitter localization accuracy using spatial spectrum inputs. It probes the model's ability to learn scene-agnostic spatial-spectral representations from unlabeled RF data and generalize across diverse indoor environments. Use when the user wants to benchmark on Indoor RF Localization Scenes, or asks about evaluating this task. Reports Euclidean distance (cm).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2512.07309
  bibtex_key: [REDACTED]
  confidence: high
---

# rf-localization-eval

> Radiance-Field Reinforced Pretraining: Scaling Localization Models with Unlabeled Wireless Signals — Wang et al. (2025) (arXiv:2512.07309, 2025)

## What this evaluates

Evaluates indoor wireless transmitter localization accuracy using spatial spectrum inputs. It probes the model's ability to learn scene-agnostic spatial-spectral representations from unlabeled RF data and generalize across diverse indoor environments.

## Datasets

- **Indoor RF Localization Scenes** — total ?; splits: test (25)

## Metrics

- `Euclidean distance (cm)` **(primary)** — range: cm
  - Euclidean distance between the predicted and ground-truth transmitter positions, reported in centimeters. Lower values indicate better localization accuracy.
- `SSIM` — range: [0, 1]
  - Structural Similarity Index measuring reconstruction quality between original and predicted spatial spectra. Ranges from 0 to 1, where higher values indicate better preservation of spectral structure.

## Input / output format

**Input**: Standardized 36×9 spatial spectrum matrix representing RF signal data.

**Output**: Predicted transmitter position coordinates.

## Scoring recipe

```python
def compute_metrics(predictions, ground_truth):
    # predictions and ground_truth are arrays of shape (N, spatial_dims)
    errors = np.linalg.norm(predictions - ground_truth, axis=1)
    mean_error_cm = np.mean(errors) * 100  # Convert to cm if inputs are in meters
    return mean_error_cm
```

## Common pitfalls

- Results vary significantly based on the proportion of labeled fine-tuning data (20%–80%).
- Pretraining masking ratio must be tuned (optimal ~75%) to avoid information loss or insufficient regularization.
- Models must be adapted to a standardized 36×9 spatial spectrum input for fair comparison.

## Evidence (verbatim from paper)

> Localization accuracy is measured by the Euclidean distance between the predicted and ground-truth transmitter positions.

## Citation

```bibtex
@misc{wang2025radiance,
  title={Radiance-Field Reinforced Pretraining: Scaling Localization Models with Unlabeled Wireless Signals},
  author={Wang et al. (2025)},
  year={2025},
  note={arXiv:2512.07309}
}
```

- arXiv: 2512.07309