sharathsrini/Kalman-Filter-for-Sensor-Fusion
A Sensor Fusion Algorithm that can predict a State Estimate and Update if it is uncertain
Why it is useful
Kalman-Filter-for-Sensor-Fusion is a recent open-source project in Machine Learning. It is included because it has recent repository activity and can support mathematical learning, research, modelling, software development, or AI workflows.
Repository at a glance
- Owner: sharathsrini
- Primary language: Jupyter Notebook
- Latest update: 2026-09-05
- Stars / forks: 155 / 34
- License: Not specified
- Links: No separate project site was listed.
Good starting points
- Read the project README and installation instructions.
- Try the smallest example before changing parameters or datasets.
- Check tests, issues, and release notes before relying on results in teaching or research.
- Cite the repository and its license when reusing code or figures.
README snapshot
Kalman filters are discrete systems that allows us to define a dependent variable by an independent variable, where by we will solve for the independent variable so that when we are given measurements (the dependent variable),we can infer an estimate of the independent variable assuming that noise exists from our input measurement and noise also exists in how we’ve modeled the world with our math equations because of inevitably unaccounted for factors in the non-sterile world.Input variables become more valuable when modeled as a system of equations,ora matrix, in order to make it possible to determine the relationships between those values.