Asking the fleet what it is doing…
monad-knowledge Wi-Fi sensing lab · FIIT STU
LOC502 active 2 / 12 weeks

BLE & RF Indoor Localization

A twelve-week track on locating people and devices indoors from radio signals — RSSI, ranging, fingerprinting, and filtering — built around the lab's own thesis direction: calibration as a periodic campaign, not a one-time fit. The throughline: every position estimate comes with an honest error distribution.

The 12-week arc

2 of 12 lectures published — each a self-contained read with a slide deck and runnable workbook. Start at the first published week and work forward.

Wk Theme & focus Read
01 RF ranging fundamentals start here RSSI, ToF, AoA — the three ways a radio measures distance/angle, and what each costs read → slides · workbook
02 Log-normal path loss The model everyone uses, its parameters, and why it drifts indoors read → slides · workbook
03 Trilateration & DOP From ranges to a position fix; the geometry that makes or breaks accuracy planned
04 Fingerprinting k-NN / probabilistic / learned radio maps, and the survey cost behind them planned
05 Filtering I: smoothing & outlier rejection Taming RSSI noise before any tracking planned
06 Filtering II: the Kalman filter State-space tracking — the workhorse, derived and implemented planned
07 Particle filters & map constraints Non-Gaussian tracking constrained by the floorplan planned
08 BLE specifics & beacon density Advertising, channels, and how many beacons a room really needs planned
09 Periodic BLE calibration campaigns Keeping a radio map alive via scheduled recalibration (thesis core). Quote the POWERED numbers, not the pilot: cross-modal disagreement tracks error at ρ = 0.68 (pilot 0.93 did not replicate; TOST vs oracle FAILED), CSI-only self-monitoring false-fires on 17 % of error-irrelevant shifts, and the trigger's honest payoff is calendar-parity at ~¼ the calibration spend — condition-based maintenance, never "event-driven beats fixed" planned
10 Sensor fusion (BLE + CSI + IMU) Combining modalities as one probabilistic estimate planned
11 Ground truth & error CDFs Why the error CDF beats RMSE, and how to collect honest ground truth planned
12 Capstone A calibrated multi-room BLE localisation pipeline, end-to-end planned

Capstone

Week 12: build a multi-room BLE localisation pipeline with a documented calibration procedure, and report its accuracy as an error CDF across rooms — including where and why it fails.