The Kalman Filter is a mathematical algorithm used to estimate the hidden state of a dynamic system from a series of noisy or uncertain measurements. It works by combining predictions from a mathematical model of the system with incoming sensor data, continuously updating its estimates in a way that minimizes error. Unlike simple averaging, the Kalman Filter accounts for both process uncertainty (how the system evolves over time) and measurement noise (imperfections in sensors), making it highly effective in real-world applications where data is imperfect.
This filter operates in two main steps: prediction and update. In the prediction step, it uses the system model to forecast the next state and its uncertainty. In the update step, it incorporates new measurements to refine the estimate, balancing trust between the model and the sensor data. Because of its efficiency and accuracy, the Kalman Filter is widely used in fields such as robotics, navigation, computer vision, and finance for tasks like object tracking, sensor fusion, and signal processing.
- Implementation in Python and C++
- State estimation from noisy measurements
- Efficient real-time updates
- Widely applicable in robotics, tracking, and navigation
python >= 3.9
gcc & g++ >= 12.3.0
Clone this repository and install numpy:
git clone https://github.com/Abolmw4/KalmanFilter.git
cd KalmanFilter
python3 -m venv <your_env>
source <your_env> activate
pip install -r requirements.txt- for working with c++ version install libeigen3-dev
apt-get install libeigen3-devKalmanFilter/
├── main.py # FastAPI service
├── config/
└── config.json
├── kalman/ # C++ module
│ ├── CMakeLists.txt
| ├── main.cpp
| ├── matrix.cpp
| ├── matrix.h
| ├── kalmanfilter.cpp
| ├── kalmanfilter.h
├── kalmanfilter/ # python module
| └── kalman_filter.py
├── test/ # Example data or test scripts
| ├── kalman_test.py
| └── services_test # Test endpoint
├── utils/ # Utility scripts/helpers
| ├── util.py
| ├── consumer.py
| ├── create_data.py
| └── producer.py
├── README.md
├── requirements.txt
└── docker-compose.yml # for runnig kafka
- for runing kafka use this command
docker compose up -d- for run FastAPI service. using host=127.0.0.1 and port 8000 and then run this commands
python3 main.py
python3 -m unittest test.services_test.MyTestCase.<each-method-you-have-test> -v- for run c++ run this command
cd kalman
mkdir build
cd build
cmake ..
make
./kalmanExample output of filtering noisy data:
