IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion
summary
Demonstrates using IPython Notebook and SymPy to derive and implement a Kalman filter for fusing multisensor data (acceleration, speed, GPS) in nonlinear dynamic models (e.g., a car). Shows how symbolic computation produces the necessary analytic matrices and how the notebooks help develop an optimal estimator for sensor fusion.
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chapters (7)
- 0:00Multisensor data fusion intro
- 7:19Kalman filter fundamentals
- 14:17Kalman filter with IPython
- 23:40Real-world applications
- 31:06Tuning filter performance
- 37:34Advanced topics
- 45:02Q&A
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