What does "Adaptive Kalman Filtering" mean?

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Adaptive Kalman Filtering is a method used to improve the accuracy of estimates in dynamic systems. It combines ideas from two approaches: the Kalman filter and recursive least squares (RLS). The Kalman filter is widely known for tracking and estimating the state of a system over time, while RLS helps adjust estimates based on new data.

In this method, the adaptive Kalman filter takes into account variations and unexpected events that might affect the system. For example, it can be used in situations where things like bumps or jolts happen, making it hard to predict the behavior of the system accurately.

By blending these two techniques, the adaptive Kalman filter makes it possible to get better estimates, even in challenging conditions where traditional methods might struggle. This is particularly useful in fields like tracking moving objects with sensors.