I've been learning about Kalman Filters, and the classic example given is tracking an object via radar/gps. My issue here is that each time you get a new data point, you update the error in the estimate, with the error always decreasing. Does this mean that Kalman Filters can't be used long-term to track a moving object?
For example, with a satellite, each measurement via GPS of the satellite location would decrease our error. Because of this and the fact that the satellite is in continuous motion, the measurement of the satellite location would slowly "fall behind" its current location. This seems to be an even bigger problem if the object in motion doesn't have a constant speed or velocity (i.g. a truck).
In more generic mathematical terms, is a Kalman Filter designed to track a set parameter, or can it be used to track a parameter that moves over time? It seems like I either have a fundamental misunderstanding about what the Kalman filter is used for or a fundamental misunderstanding of the equations.