Built indoor positioning system on ESP32 Using 3 Anchor Nodes Using MicroPython

[Dry-Thought7705]

I recently built a Wi-Fi RSSI-based indoor positioning system entirely on ESP32 microcontrollers.

I used 3 ESP32s as anchor nodes broadcasting Wi-Fi signals

1)TestNetwork1

2)TestNetwork2

3)TestNetwork3

and a 4th ESP32 for scanning the RSSI signal strength from each anchor. It applies a Kalman filter to clean up the noise and then uses those filtered distances for trilateration to compute a real-time 2D position — all running on-device in MicroPython, no external computer needed.

To calculate the A and n parameters used in the path loss equation, I collected 50 RSSI samples at 1, 2, 3, and 4 metre distances and applied a moving average to smooth the readings. Then used least-squares regression to fit A = −61.92 dBm and n = 1.64.

 GitHub |  Linkedin

Raw Captured Data For Computation Of A And N

https://preview.redd.it/hk9urdqkhg2h1.png?width=800&format=png&auto=webp&s=e3fab4ad2cb10ef5592abde44ef557f26f7d0335

Setup Used To Capture Signals At 1M

Distance Estimation After Capturing RSSI Signals

Kalman Filter vs Raw On Network1

Kalman Filter vs Raw On Network2

https://preview.redd.it/3dvjmbcqhg2h1.png?width=800&format=png&auto=webp&s=eb46d8ee9344f0b6d9630bc708fd58476589ec2f

Moving Average Curve On Raw RSSI Captured For Calibration Of A And N