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designer to easily determine the dominant error sources and decide whether it is feasible to reduce the error e ects (through either better hardware or more detailed modeling) to the point where the design speci cations are achieved This process is referred to as error budgeting Either eqns (615-621) or eqns (626-627) can be used for error budgeting analysis Note that when K is xed, both sets of equations are linear in P0 , Qd, and R (or M0 , Qd, and R) For brevity, this discussion only considers eqns (626-627) with Q and R being time-invariant Since this set of equations is linear in each of the driving noise terms, the principal of superposition can be applied lq lm lr Let M0 = i=1 Mi (0), Q = i=1 Qi , and R = i=1 Ri The symbols lm , lq , and lr denote the number of independent error or noise terms in the matrices M0 , Q, and R, respectively, and each of the matrices Mi (0), Qi , and Ri are symmetric and positive de nite For simplicity of notation, let each of these matrices be diagonal The design eqns (65-68) are rst simulated once, with all noise sources turned on, to determine the gain sequence Kk This gain sequence must be saved and used in each of the subsequent component simulations of the error budget analysis The components of Pk that are of interest should be saved at the corresponding times of interest, for later analysis For the subsequent l = lm + lq + lr simulations of eqns (626-627), the gain vector sequence is considered as given For each independent component of M0 (ie, l = 1, , lm ), we calculate M (k + 1) l M+ l = M+ (k) l = I KH M l I H K (628) (629).

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with Ml (0) being the l-th independent component of M(0) as previously de ned This set of simulations produces the error covariance due to each independent source of initial condition uncertainty in the absence of process and measurement noise For each independent component of Qd (ie, l = (lm +1), , (lm +lq )), we calculate M (k + 1) l M+ l = M+ (k) + Qdi l = I KH M l I H K (630) (631)

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with Ml (0) being a zero matrix and Qdi being the discrete-time equivalent of Qi , where i = l lm This set of simulations produces the error covariance due to each independent source of process driving noise in the absence of initial condition errors and measurement noise For each independent component of R (ie, l = (lm + lq + 1), , (lm + lq + lr ), we calculate M (k + 1) l = M+ (k) l (632)

CHAPTER 6 PERFORMANCE ANALYSIS I KH M I H K l + KRi K (633)

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with Ml (0) being a zero matrix and i = l lm lq This set of simulations produces the error covariance due to each independent source of measurement noise in the absence of initial condition errors and process driving noise At the completion of the (lm + lq + lr )-th covariance simulation, each independent source of system error has been accounted for exactly once By the principle of superposition, for each k the total error covariance from all sources Pk is computed as

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Apr 18, 2013 · Viewing PDF in Windows forms using C# How to open .Pdf file in C#.Net Win form Loading a ...Duration: 6:08 Posted: Apr 18, 2013

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