Kalman Filters: Theory, Applications, Implementation, and Considerations

Explore the power of Kalman filters in state estimation and sensor fusion. Understand their theoretical foundations, versatile applications in navigation, robotics, finance, and signal processing, and learn how to implement them using Python and C. Delve into their strengths, limitations, and considerations, and gain hands-on experience through practical examples. Uncover the mathematical framework behind Kalman filters and harness their potential to enhance accuracy in estimating dynamic system states amidst noise and uncertainties.