Description
Calibration is any procedure that ties a sensor's output to a ground-truth scale or that refits a model's parameters to a new environment. The thesis core hypothesis — BLE-calibrated CSI crowd sensing — is built around periodic calibration campaigns in which BLE-derived trajectories supply ground-truth density and flow against which CSI inference is anchored. This note is the umbrella; specific variants (data-assimilation, domain-adaptation, fingerprint re-survey) are the implementation tactics.
When it's used
- Periodic re-anchoring of CSI inference from BLE ground truth
- Indoor-positioning fingerprint re-survey
- Crowd-model parameter fitting (e.g.
social-force-modelparameters from real trajectories) - Sensor-fusion bias correction
Limitations
- Calibration is only as good as the ground-truth signal feeding it
- Re-calibration cadence vs cost is a deployment trade-off
- Drift between calibrations is the residual error budget