Introduction To Kalman Filter

The Kalman filter predicts the position coordinates and velocity of an object from a limited set of observations of the position of the object containing the noise. It has strong robustness. Even if there is an error in the observation of the position of the object, we can estimate the position of the object more accurately based on the historical state of the object and the current observation of the position. The Kalman filter is mainly divided into two phases: the prediction phase predicts the current position information based on the position information of the last time point; the update phase corrects the position prediction by the current observation of the object position, thereby updating the position of the object.

As a concrete example, suppose your home has a power outage, no lights, and you want to walk back to the bedroom from the living room. You know the relative position of the living room and the bedroom, so you walk in the dark and try to predict the current position by counting the steps. Halfway through, you touched the TV. Since you know in advance the approximate location of the TV in the living room, you can correct your prediction of the current position by the position of the TV in your impression, and then continue to rely on the calculation step based on this adjusted more accurate position estimate. The number goes to the bedroom. By counting the number of steps and touching objects, you end up walking back to the bedroom from the living room. The rationale behind this is the core principle of the Kalman filter.

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