Kalman-Filters

.............................. (This is a part of the CVND course that I did at Udacity! ) ..............................

Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

Let's take the case of a robot that moves through the world. As a robot moves through the world it locates itself by performing a cycle of:

sensing and performing a measurement update and moving and performing a motion update

How to run:

1. Open Jupyter Notebook in your browser
2. Open 1D Kalman Filter, solution.ipynb
3. Run each block one by one.

Please refer to my blog to know more: https://medium.com/analytics-vidhya/kalman-filters-a-step-by-step-implementation-guide-in-python-91e7e123b968?sk=530a8c1e099c047cac45cd0f945ce4ab