Kalman Filtering: Uses & Benefits

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Kalman Filtering is an optimal technique for recovering signals from noise, widely used in applications like satellite communication to extract signals obscured by interference. It serves as the optimal estimator for linear systems by minimizing the error covariance matrix. The extended Kalman filter is employed in nonlinear systems, such as radar and sonar tracking. This method allows for the estimation of velocity and acceleration from noisy position measurements. Overall, Kalman Filtering is crucial for enhancing signal clarity in various technological fields.
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What is it and where is it used ?
 
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It is the optimal filtering technique to recover the maximum information
about a signal in the presence of noise.

It is used (for example) to extract the signals from distant space satellites
when they are covered up by lots of noise.
 
In addition of what Antiphon wrote:
When we say that some system is optimal, it is optimal relative to some parameter. Kalman filter is the optimal estimator for a linear system in the sense of minimization of the error covariance matrix.
The extended Kalman filter, is a suboptimal estimator used in nonlinear systems. for instance radar and sonar tracking systems. One important characteristic of the KF is that you can estimate velocity and acceleration of a dynamic system from noisy measurements of it's position.
 
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