Page 317 - Classification Parameter Estimation & State Estimation An Engg Approach Using MATLAB

306 STATE ESTIMATION IN PRACTICE

In discrete time this becomes x(i þ 1) ¼ (1 /(RC))x(i). The
sampling period is ¼ 0:1(ms). K ¼ 100. No prior knowledge about
x(0) is available.
Figure 8.16 shows observed measurements along with the corres-
ponding estimated states and the result from the Rauch–Tung–Striebel
smoother. Clearly, the uncertainty of the offline obtained estimate
is much smaller than the uncertainty of the Kalman filtered result.
This holds true especially in the beginning where the online filter has
only a few measurements at its disposal. The offline estimator can
take advantage of all measurements.

8.6 REFERENCES

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