Key concepts:
The Kalman filter is a recursive algorithm that estimates the state of a system from a series of noisy measurements. It was first introduced by Rudolf Kalman in 1960 and has since become a widely used algorithm in many fields. The Kalman filter is based on the idea of predicting the state of a system at a future time using a model of the system's dynamics, and then updating the estimate using new measurements. Key concepts: The Kalman filter is a recursive
Before jumping into the full Kalman equations, it's essential to understand recursive expressions. A recursive filter uses the previous estimate and a new measurement to calculate the current estimate, rather than storing a massive history of data. Key concepts: The Kalman filter is a recursive