The Kalman filter is a mathematical algorithm that uses a combination of prediction and measurement updates to estimate the state of a system. It's widely used in various fields, such as navigation, control systems, signal processing, and econometrics.
Your physics equations can predict where the rocket should fly, but wind gusts and atmospheric changes cause drift. The Kalman filter is a mathematical algorithm that
The Kalman filter operates in a continuous, recursive loop consisting of two primary phases: and Update . It does not need to store a massive history of past data; it only needs the estimate from the previous time step to calculate the next one. The Kalman filter operates in a continuous, recursive
The filter uses the current state to predict the state in the next time step. It also projects the state error covariance (the measure of uncertainty). 2. Compute Kalman Gain The Kalman Gain ( ) is a weighting factor between 0 and 1. If your sensors are highly accurate, is close to 1 (the filter trusts the measurement). If your sensors are highly noisy, is close to 0 (the filter trusts the physics prediction). It also projects the state error covariance (the
Phil Kim’s book stands out because he refuses to skip the fundamentals. He assumes you know basic MATLAB and high school algebra. That’s it.