Tracking the Unseen: An Introduction to the Kalman Filter and Its Use Cases

The Kalman filter is a powerful tool for modeling and estimating the state of dynamic systems. It is widely used in a variety of fields, including engineering, economics, and robotics, and has proven to be particularly useful for tracking objects or processes that are subject to noise and uncertainty. At its core, the Kalman filter is an algorithm that uses a series of measurements observed over time to estimate the underlying state of a system. It does this by combining the measurements with a mathematical model of the system, taking into account the uncertainties in both the measurements and the model. The Kalman filter has a number of attractive features that make it well-suited to a wide range of use cases. It is computationally efficient, easy to implement, and can handle both linear and nonlinear systems.