kalbee 0.2.0
A clean, modular Python implementation of Kalman Filters and estimation algorithms.
Requires Python: >=3.8
Keywords: kalman-filter, extended-kalman-filter, ekf, unscented-kalman-filter, ukf, particle-filter, ensemble-kalman-filter, information-filter, state-estimation, sensor-fusion, tracking, alpha-beta-gamma, rts-smoother, adaptive-filter, robotics, signal-processing, python

# kalbee 🐝

<div align="center">
  <img src="https://raw.githubusercontent.com/MinLee0210/kalbee/main/docs/kalbee.png" alt="kalbee logo" width="300"/>
</div>

<br>

`kalbee` is a clean, modular Python implementation of Kalman Filters and related estimation algorithms. Designed for simplicity and performance, it provides a standard interface for state estimation in various applications.

## ✨ Features

- **8 Filters**: KF, EKF, UKF, Particle, Ensemble, Information, Alpha-Beta-Gamma, Adaptive KF
- **RTS Smoother**: Rauch-Tung-Striebel backward smoother for post-processing
- **Metrics**: RMSE, NEES, NIS, Log-Likelihood for filter diagnostics
- **Experiment Runner**: Compare filters on synthetic signals with one line
- **AutoFilter Factory**: Switch between filters by name
- **Numerical Stability**: Joseph form covariance updates, symmetry enforcement
- **NumPy/SciPy Integration**: Optimized for numerical computations

## 🚀 Installation

```bash
pip install kalbee
```

Or from source:

```bash
git clone https://github.com/MinLee0210/kalbee.git
cd kalbee
pip install -e .
```

## 🛠️ Quick Start

### 1. Standard Kalman Filter

```python
import numpy as np
from kalbee import KalmanFilter

state = np.zeros((2, 1))  # [position, velocity]
cov = np.eye(2)
F = np.array([[1, 1], [0, 1]])  # Constant velocity model
Q = np.eye(2) * 0.01
H = np.array([[1, 0]])
R = np.array([[0.1]])

kf = KalmanFilter(state, cov, F, Q, H, R)
kf.predict()
kf.update(np.array([[1.2]]))
print(f"Estimated State:\n{kf.x}")
```

### 2. Compare Filters with Experiments

```python
from kalbee import run_experiment

report = run_experiment(
    signal="sine",
    filters=["kf", "ekf", "ukf", "pf"],
    noise_std=0.5,
)
print(report.summary())
```

### 3. AutoFilter Factory

```python
from kalbee import AutoFilter

kf = AutoFilter.from_filter(state, cov, F, Q, H, R, mode="kf")
# Available modes: kf, ekf, ukf, abg, pf, enkf, if, akf
```

## 📚 Documentation

Full documentation with theory, code examples, and experiments for each filter:

```bash
pip install mkdocs-material
mkdocs serve
```

- [Getting Started](docs/getting_started.md)
- **Filters**: [KF](docs/filters/kalman_filter.md) · [EKF](docs/filters/extended_kalman_filter.md) · [UKF](docs/filters/unscented_kalman_filter.md) · [PF](docs/filters/particle_filter.md) · [EnKF](docs/filters/ensemble_kalman_filter.md) · [IF](docs/filters/information_filter.md) · [ABG](docs/filters/alpha_beta_gamma_filter.md) · [AKF](docs/filters/adaptive_kalman_filter.md)
- **Features**: [RTS Smoother](docs/features/rts_smoother.md) · [Metrics](docs/features/metrics.md) · [Experiments](docs/features/experiments.md)
- [Architecture](docs/architecture.md)

## 🧪 Testing

```bash
uv run pytest tests/   # 58 tests
```

## 📄 License

This project is licensed under the Apache License 2.0.

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Check `TODO.md` for ideas.