Kalman Filters are mathematical algorithms used for estimating the state of a dynamic system from a series of incomplete and noisy measurements. They operate recursively, meaning they update their estimates as new data becomes available, making them particularly useful in real-time applications. The filter produces estimates of unknown variables by minimizing the mean of the squared errors, thus providing a statistically optimal solution. Common use cases include navigation systems, robotics, and time series analysis, where accurate predictions of future states are crucial.
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