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What is it and where is it used ?
Kalman Filtering is a mathematical algorithm that uses a series of measurements over time to estimate the true value of a state or variable that is subject to random fluctuations. It can be used to improve the accuracy and precision of measurements, and is commonly used in fields such as engineering, physics, and computer science.
Kalman Filtering offers several benefits, including improved accuracy and precision of measurements, the ability to handle noisy or incomplete data, and the ability to incorporate prior knowledge or predictions into the estimation process. It can also be used to track the state of a system over time, even if the measurements are not available at every time step.
Kalman Filtering has a wide range of applications, including navigation and tracking systems, control systems for autonomous vehicles, signal processing, and image or video processing. It is also commonly used in data fusion, where multiple sensors are used to estimate a single state or variable.
Kalman Filtering works by combining two sources of information: predictions based on a mathematical model of the system and measurements from sensors. The algorithm uses a series of equations to calculate the most probable state of the system at each time step, taking into account the uncertainty in both the predictions and the measurements.
While Kalman Filtering offers many benefits, it also has some limitations. It assumes that the system being modeled is linear and that the uncertainties in the measurements and predictions are normally distributed. It also requires knowledge of the system's dynamics and measurement model, which may not always be available. Additionally, Kalman Filtering may not perform well in highly nonlinear systems or when there are sudden changes or outliers in the measurements.