Sensor fusion in Python

sensorfusion-tools (imported as sftools) is a Python library for students learning estimation, filtering, and sensor fusion.

The library aims to make the structure of estimation problems visible. Its APIs favor explicit workflows over high-level shortcuts, helping learners understand each step. It uses widely adopted scientific Python packages: NumPy and SciPy for numerical work, and Matplotlib for plotting.

Use it to build dynamic and observation models, work with Gaussian and particle states, run least-squares estimators and Kalman or particle filters, and propagate uncertainty with nonlinear moment transforms.

A tracking example

A constant-velocity Kalman filter tracks a simulated target from noisy position measurements. The plot shows the true path, measurements, estimate, and selected 95% confidence ellipses.

Explore a similar tracking example in the documentation.

Start here

• Getting started — install the library and run a first estimate.
• Documentation — explore states, models, estimators, transforms, and plotting.