feat: Add BLE trilateration indoor positioning example.

[nedseb]

Target repository

This example should be created in micropython-steami-sample, not in micropython-steami-lib. BLE examples are not tied to a specific driver and belong in the sample repo.

What

Create an example that estimates the position of a mobile STeaMi board in a room using RSSI trilateration from 3 fixed beacon boards.

Why

Core of the indoor localization project. Applies the log-distance path loss model to convert RSSI to distance, then uses trilateration to compute a 2D position. This is the real-world application that ties together all previous BLE examples.

Suggested approach

1. 3 beacon boards at known positions (e.g. corners of a triangle, coordinates hardcoded)
2. Mobile board scans all 3 beacons simultaneously, reads RSSI for each
3. Convert RSSI to distance using the log-distance path loss model:
   `d = 10 ^ ((RSSI_ref - RSSI) / (10 * n))`
   where `RSSI_ref` is the RSSI at 1 meter (calibrated) and `n` is the path loss exponent (~2-3 indoors)
4. Trilaterate: solve the intersection of 3 circles to estimate (x, y)
5. Display the estimated position on the OLED as a dot on a simple 2D map
6. Show the 3 beacon positions as fixed markers

Learning goals

• Path loss model and its parameters
• Trilateration algorithm (least-squares or geometric)
• Calibration procedure (measure RSSI_ref at 1 meter for each beacon)
• Error analysis (why is indoor positioning imprecise?)

Hardware

• 4 STeaMi boards (3 beacons + 1 mobile)
• SSD1327 OLED on mobile board
• Meter stick / tape measure for calibration

Depends on

• feat: Add BLE RSSI room mapper example. #412 (RSSI mapper — calibration data)
• feat: Add BLE RSSI proximity detector example. #410 (RSSI proximity — scanning logic)

Documentation

Write a pedagogical activity sheet for the STeaMi wiki covering:

• Learning objectives (propagation model, trilateration, calibration, error sources)
• Calibration procedure (measure RSSI at 1m for each beacon pair)
• Mathematical background (path loss formula, trilateration equations)
• Expected accuracy and discussion of limitations (multipath, obstacles, antenna orientation)
• Extension ideas (fingerprinting, Kalman filtering, adding a 4th beacon)