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Figure 61: Results of the covariance analysis of Example 61 for R = 10 m2 The standard deviation of each component of the estimated state is plotted versus time in the left column Actual performance is indicated by the bold dotted curve The performance predicted by the implemented lter is the narrow solid curve The right column plots the Kalman lter gains versus time previous case The lter trusts the lter state, which represents a time average of all past measurements more than the measurement The performance prediction by the lter is still optimistic, p = 7m predicted in steady-state while p = 25m is achieved, because the model does not properly account for the time correlation of the position measurement error The lter is not averaging white noise corrupted measurements, but measurements with a highly correlated error The actual (correlated error) will not average out over any reasonable time span A better method for addressing the sensor bias of this example without including it as an augmented state is discussed in Section 5102 In the special case where the state de nitions are the same and it is reasonable to assume that = and H = H with V = I, the error state

Question 6-6

1 8 3 45 6

How can we quickly add two fractions m /n and p /q, where m and p are integers, and n and q are positive integers

10 0 15 0

Answer 6-6

1 05 0 K2

7 8 6

We can add these two fractions like this: m /n + p /q = (mq + np)/nq We can also express this in words: Multiply the numerator of the first fraction by the denominator of the second Multiply the denominator of the first fraction by the numerator of the second Add these two products together Divide this sum by the product of the denominators

01 005 0 2 x 10

Part One Question 6-7

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Figure 62: Results of the covariance analysis of Example 61 for R = 625 m2 The standard deviation of each component of the estimated state is plotted versus time in the left column Actual performance is indicated by the bold dotted curve The performance predicted by the implemented lter is the solid narrow curve The right column plots the Kalman lter gains versus time x = (x x) can be de ned and the covariance matrix propagation equations for the error state simplify considerably to M k+1 M+ k = = M+ + Qdk k I Kk Hk M I Hk Kk k + Kk Rk Kk (626) (627)

How can we quickly subtract a fraction p /q from a fraction m /n, where m and p are integers, and n and q are positive integers

Table 22-1

Answer 6-7

where M = cov( , x) These equations will be used to support the discusx sion of Section 63 In that section, to simplify the notation, all subscript k s in eqn (627) will be dropped To test the robustness of the lter design to model assumptions, the covariance analysis may be repeated for various instances of the design model assumptions For example, because the values of Qdk and Rk in eqns (66-68) and Qdk and Rk in eqns (626-627) can be distinct, the

The difference can be found this way: m /n p /q = (mq np)/nq Stated in words: Multiply the numerator of the first fraction by the denominator of the second Multiply the denominator of the first fraction by the numerator of the second Subtract the second product from the first Divide this difference by the product of the denominators

latter set of equations can be used to study the state estimation accuracy versus the variance of a component of the process or measurement noise This leads into the idea of Monte Carlo analysis that is brie y discussed in Section 62

Color Band Black Brown Red Orange Yellow Green Blue Violet Gray White Gold Silver No color 1st Digit 0 1 2 3 4 5 6 7 8 9 2nd Digit 0 1 2 3 4 5 6 7 8 9 Multiplier 1 10 100 1,000 (K) 10,000 100,000 1,000,000 (M) 10,000,000 100,000,000 1,000,000,000 01 001 5% 10% 20% 1% 2% 3% 4% Tolerance

Question 6-8

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