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开源软件介绍(OpenSource Introduction)：

Localize your cat at home with BLE beacon, ESP32s, and Machine Learning

tl;dr / abstract. System which can be used for localization the position of a cat in a building using bluetooth low energy (BLE) beacons attached to the object, a set of cheap ESP32 detectors, and Machine Learning models.

• How it works
• Hardware setup
• Software setup
  • BLE beacon
  • Detectors
    • ESP32
    • Raspberry Pi
  • On the server
    • 0. Data source
    • 1. Python environment
    • 2. Program for cat localization prediction
• Training and testing of ML models
  • Data collection
  • Algorithms evaluation and models training
  • Conclusions
• Detecting
• Tips and tricks
• Improvements of the accuracy
• Further directions
• Acknowledgements

How it works

This is an overwiev of a pipeline for creating an inhouse cat locator. Actually, it can be applied to any animal (including humans) or object, and any building. The system works as follows:

• The cat with a small BLE beacon, is emiting BLE signals
• BLE signal is detected by ESP32s located here and there; they are measuring he signal strength of the BLE beacon.
• Each ESP32 sends data to the server (database)
• The python program is fetching the last measurements from all ESP32 detectors (i.e., signal strenght values)
• Using trained machine learning models it predicts the location of the cat