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Optimal measurement budget allocation for Kalman prediction over a finite time horizon by genetic algorithms
2021
EURASIP Journal on Advances in Signal Processing
AbstractIn this paper, we address the problem of optimal measurement budget allocation to estimate the state of a linear discrete-time dynamical system over a finite horizon. More precisely, our aim is to select the measurement times in order to minimize the variance of the estimation error over a finite horizon. In addition, we investigate the closely related problem of finding a trade-off between number of measurements and signal to noise ratio.First, the optimal measurement budget allocation
doi:10.1186/s13634-021-00732-8
fatcat:lp2nat3bovhrblhhxogc6hmzxe