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Kalman filter is a classic estimation method, which is widely used in the problem of estimating the state of the system. This blog will introduce the basic principle of the Kalman filter, and demonstrate how to use the Kalman filter to estimate the position of a moving object through a simple Python code example.
By running the code, we will get an image with two subplots showing the estimated position and velocity over time. It can be seen from the image that the Kalman filter can estimate the position and velocity of the moving object very well, while considering the influence of measurement noise and process noise, making the estimation result more stable and accurate.
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1. Introduction to Kalman filter
The Kalman filter is a recursive estimation filtering algorithm for estimating the state of a linear dynamic system. It combines measurements and state models, and performs state estimation in a recursive manner. The core idea of the Kalman filter is to use prior estimation and measurement update to estimate the state of the system in an optimal way, taking into account the