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EURASIP Journal on Advances in Signal Processing
Fig. 3 | EURASIP Journal on Advances in Signal Processing

Fig. 3

From: Optimal measurement budget allocation for Kalman prediction over a finite time horizon by genetic algorithms

Fig. 3

a Evolution of a particular realization of y(t) and its estimates with both regular Kalman predictor (Regular) and optimal intermittent Kalman predictor (GA). b Mean squared prediction error with respect to time and 95% quantile over 100,000 realizations for the regular Kalman predictor (Regular) and for our optimal intermittent Kalman prediction method (GA). Regular measurement times and the GA measurement times are also printed on both graphs. Simulations are realized on the system described by Eqs. (31) and (32) with T=100 time steps and N=5 measurements

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