Indoor Positioning System Using Bluetooth RSSI and Trilateration

Overview

This project implements an indoor positioning system (IPS) using Bluetooth Low Energy (BLE) technology. It utilizes RSSI (Received Signal Strength Indicator) values from three ESP32-S3 Feather modules acting as beacons to calculate the position of a target device through trilateration. The system processes these signals to provide real-time positioning within an indoor environment.

Figure: Layout of the experiment area

Features

• Bluetooth RSSI Collection: Collects RSSI data from multiple ESP32-S3 Feather modules using MQTT.
• Kalman Filtering: Applies Kalman filtering to the RSSI data to enhance accuracy.

Figure: Line graph showing how the Kalman filter works

• Trilateration Algorithm: Employs trilateration to compute the position based on RSSI values.
• Real-time Visualization: Uses Matplotlib to display the positioning and RSSI data dynamically.

Figure: Graphical display run when the server is run, showing the trilateration and RSSI values received

• MQTT Communication: Leverages MQTT for efficient data transmission between the beacons and the central server.

Hardware Requirements

• 3 x ESP32-S3 Feather Boards
• 1 x PC or Laptop
• 1 x Pixel Display

Figure: Example of what the pixel display will look like when the server is run

• 1 x BLE-capable Device (e.g., Headphones)

Setup and Installation

1. Set up the MQTT Broker: Install and configure Mosquitto on your server. You can use the Docker Compose file provided in the /mosquitto directory.
2. Configure the ESP32-S3 Boards: Flash the ESP32-S3 boards with MicroPython and set them up to transmit RSSI data via MQTT. The necessary code for this can be found in the /beacons directory.
3. Run the Server: Start the server application on your PC to receive data and perform calculations. First, create a virtual environment (venv) and install the requirements.txt. Then, run the Python file located at src/server.py.

Usage

Start the system by powering up the ESP32-S3 boards and the target BLE device. Ensure the MQTT broker is running, and execute the server script to begin receiving and processing data. The real-time location of the target device will be displayed on the pixel display and the Matplotlib graph.

Figure: Sequence diagram/flow of the solution

Future Enhancements

• Integration of additional beacons to improve coverage and accuracy.
• Exploration of alternative technologies such as Zigbee and Wi-Fi.
• Advanced filtering techniques to further refine signal processing.

Acknowledgements

Parts of this project make use of the python3-idotmatrix-client library for communicating with the pixel display.