KalmanFilter-Snippets

Kalman Filter Implementation

This project implements a Kalman Filter to process data from sensors. The Kalman Filter is a powerful tool for estimating the state of a system based on noisy measurements. This implementation demonstrates how to initialize, predict, and update the state estimates using the Kalman Filter.

Table of Contents

• Overview
• Requirements
• Setup
• Usage
• Output
• Explanation

Overview

The Kalman Filter is implemented in c++ and is designed to handle a state vector of arbitrary dimensions. The filter can be used to process sensor data and provide estimates of the true state of the system.

Requirements

• gcc compiler with c++11 support
• Matplotlib

Setup

1. Clone the repository:

   git clone https://github.com/RaghuTheFire/kalman-filter.git
   cd kalman-filter

2. Install the required packages:

   pip3 install numpy matplotlib

Usage

1. Run the kalman_filter.cpp script to see the Kalman Filter in action:

g++ kalman_filter.cpp -std=c++11 -I /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/include/python3.7m -I /usr/local/lib/python3.7/site-packages/numpy/core/include -L /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib -lpython3.7

2. The script will simulate some sensor data, process it using the Kalman Filter, and display the results.

Output

The output will include plots of the measurements and the Kalman Filter predictions for each component of the state vector.

Explanation

References

• Understanding the Basis of the Kalman Filter via a Simple and Intuitive Derivation
• How a Kalman Filter Works in Pictures
• Kalman Filter
• Understanding Kalman Filters with Python