CrossrefISIGoogle Scholar12. 196 — 204 . Unlike the \( \alpha -\beta -(\gamma) \) filter, the Kalman Gain is dynamic and depends on the precision of the measurement device. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem [1]. Math. A.
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China Inf. Simons, From Hahn-Banach to Monotonicity, Springer, New York, 2008. , 26 ( 2013 ) pp. , the weight of the estimate and the measurement weight are equal. Appl.
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As mentioned above, the initialization is performed only once, and it provides two parameters:
read The initialization is followed by prediction.
Now we understand the Kalman Filter algorithm, and we are ready for the first numerical example
Assume that we would like to estimate the height of a building using a very imprecise altimeter. Delbaen, S.
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R. We get faster Kalman Filter convergence if we initialize with a more meaningful value. Finance , 7 ( 1997 ), pp. Control , 36 ( 1991 ), pp.
In our first example (gold bar weight measurement), the system’s dynamics is constant. 152 — 166 .
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The existence and uniqueness results of the MMSE are deduced. Math. J. The variance of the measurement errors could be provided by the scale vendor or derived by a calibration procedure. 5.
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Before the first iteration, we must initialize the Kalman Filter and predict the following state (which is the first state).
official website The measurement is performed for every filter cycle, and it provides two parameters:
In addition to a measured value, the Kalman filter requires a measurement uncertainty parameter. visit site