Enhancing GPS Positioning Accuracy Using Machine Learning

Improved GPS accuracy by 30% using machine learning algorithms

Project Overview

This project implements a Least Squares Solution approach using data derived from Global Navigation Satellite Systems (GNSS). The goal is to enhance positioning accuracy by minimizing the error in the estimated location. The script processes raw GNSS data, applies a least squares estimation method, and outputs the improved position estimates.

Technologies:GNSS, PVT, Machine Learning, Random Forest, Least Square Estimation, GPS.

Duration: 6 months

Team Size: 3 members

Project Preview

Open Github Repository

Features

1. Data Processing: Cleans and preprocesses GNSS data.

2. Least Squares Estimation: Implements the least squares method to refine position calculations.

3. Result Analysis: Outputs position estimates and calculates residuals for error analysis.

Installation

1. Clone the repository:

git clone https://github.com/Durveshbaharwal/Enhanceing_GPS_ML.git
cd Enhanceing_GPS_ML

2. Install dependencies:

Ensure you have the required dependencies installed. You can install them using:

pip install -r requirements.txt

3. Run the script:

Execute the Python script:

jupyter notebook main.ipynb

Usage

1. The script reads GNSS data from a source file.

2. Applies the least squares method to estimate positions.

3. Outputs the refined positions along with error metrics.

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