................................. (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:
- Open Jupyter Notebook in your browser
- Open 1D Kalman Filter, solution.ipynb
- 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
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