Kalman Filters
1. Kalman Filters are algorithms that take data inputs from multiple sources and estimates unknown variables, despite a potentially high level of signal noise.
2. Kalman Filter is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.
Often used in navigation and control technology, the Kalman Filters have the advantage of being able to predict unknown values more accurately than if individual predictions are made using singular methods of measurement.
Kalman Filters use a two-step process for estimating unknown variables. The algorithm works by first estimating the current state variables, and measures their uncertainties. Then, the algorithm updates the estimates using a weighted average, wherein more weight is attributed to estimates with higher levels of uncertainty. Because the filter takes in information from multiple sources, both current state and predicted state, the filter is able to provide a level of accuracy higher than if estimates were made given only one of the multiple sources.
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