Easy Derivation of the Kalman Filter from Scratch by Using the Recursive Least Squares Method
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In this video tutorial and in the accompanying post, we derive the Kalman filter equations by using the recursive least squares method. We first, introduce a priori and a posteriori state estimates. We then introduce covariance matrices of estimation error. Then we explain how to propagate the mean of the state and covariance matrices over time by using the system model. Finally, we use the recursive least squares method to derive the Kalman filter equations.
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