Indoor Positioning System with Kalman Filter

This project implements a basic indoor positioning system using trilateration and Kalman Filter smoothing in Python. It was created as part of preparation for contributing to the Movement project under Google Summer of Code (GSoC) 2025.

📌 Project Overview

The goal is to estimate the position of a moving object indoors by using distance measurements from three fixed beacons (A, B, C). We apply:

• Trilateration: Calculates raw position estimates from distances.
• Kalman Filtering: Smooths the noisy position estimates over time.

📁 Dataset :

A CSV file contains the measured distances to three beacons (Distance A, Distance B, Distance C) at each timestamp. Example:

Distance A,Distance B,Distance C
2.1, 3.9, 2.8
...

Beacon coordinates:

• A: (0, 0)
• B: (5, 0)
• C: (2.5, 5)

🛠️ Installation

Create a virtual environment and install dependencies:

pip install -r requirements.txt

▶️ How to Run :

Open the notebook:

jupyter notebook Indoor_Positioning_System.ipynb

Run all cells to perform:

• Distance-based trilateration
• Kalman Filter smoothing
• Visualization

📊 Output Insight :

The final plot shows:

• Raw estimated positions from trilateration (blue dots)
• Smoothed trajectory using Kalman Filter (orange line)

This shows how filtering reduces noise in indoor location tracking.

📄 GSoC Use :

This project demonstrates:

• Experience with Kalman Filters and state estimation
• Working with noisy sensor data
• Real-world use cases of motion smoothing

It aligns well with the Movement GSoC project on Kalman filters for animal tracking or indoor positioning.

👨‍💻 Author :

[Kaustubh Dangche]
Master’s in Data Science at Illinois Institute Of Technology, USA
GitHub: [Kaustubhdangche]

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