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De ning the error state vector as x = x x, by linearizing the di erence between eqns (710) and (712), the dynamic equation for the state error vector is 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 u 0 0 x + 1 0 0 (715) x = 0 0 0 0 1 0 0 0 0 y 0 0 0 0 0 0 0 0 0 In the derivation of this model, in the standard form of eqn (457), the elements F23 , F25 , and G23 warrant additional discussion These terms are a linearization of u + x3 a 1 x5 evaluated for x near x Using the fact that for | x| the linearization proceeds as follows a u + x3 1 x5 = a (u + x5 u + x3 + x3 x5 ) a (a x5 a x3 1 + x5 u + x3 + x3 x5 ) (716) 1,

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May-10

These child-care facilities are organized and operated by school districts, cities, towns, counties, and states or provinces In some cases, they take the form of recreation programs run during vacations or special holiday periods In a big city, hundreds of these government-sponsored facilities care for thousands of children Transit companies have realized that encouraging day-care centers to open near stations attracts riders A $15 million childcare center was constructed near the Shady Grove station of the Washington Metropolitan Transit Authority San Francisco s transit authority is urging developers to build day-care centers near its stations, while the New York Metropolitan Transportation Authority is studying child-care sites on its railroads

1 1 x

Jun-10

(1 + x)

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where u x5 = x5 a x3 x5 x5 u to rst order Eqn (716) results in the second row of the F and G matrices The aiding output error model is the linearization of the di erence between eqns (75) and (714) This linearization yields yk = x1 (k) + x4 (k) + 2 (k) = H xk + 2 (k)

7/02/2010

(717)

Aug-10

where H = [1, 0, 0, 1, 0]

Each subsection that follows describes one aspect of the estimator design 7251 Observability Analysis

1200 1175 1150 114750 1125 1100 1075 107725 1050 1025 R PercentR (14) = 1789 100 75 Williams %R (14 Days) 50 25 1789 0 Sep-10

To initiate the estimator design, we perform an analysis of eqns (715) and (717) to determine the conditions under which the error state is observable

72 METHODOLOGY: DETAILED EXAMPLE Using the results of Section 361, the observability matrix is 1 0 0 1 0 0 1 0 y 0 2 u O= 0 0 1 y 0 0 0 3 0 y 4 0 0 0 y 0

12 11

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The rst four states are observable because y > 0 The accelerometer scale factor error is only observable over time intervals during which u(t) = 0 Because the application is expected to satisfy this condition frequently, we expect to be able to estimate the error state vector The accuracy of the estimate cannot be assessed without further analysis Kalman lter design and subsequent performance analysis will require the discrete-time equivalent model Note that formally this is an analysis of the observability of the error state x, not the state x or the estimated state x In fact, x is computed by the navigation system and is known; however, the fact that x is known does not mean that it is an accurate representation of x Because x = x x, the observability of x implies that we can maintain the accuracy of x as we can estimate x and then correct x using the assignment x + x When x is not observable it is possible for the unobservable portion of x to become large, which implies that the corresponding unobservable portion of x is not accurate 7252 Computation of and Qd

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