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This approach is analogous to the idea of a complementary lter and is further discussed in Sections 51053 and 73 The linearized system is typically time-varying, see eqns (599) and (5102) The matrix k is the state transition matrix de ned in eqn (354) corresponding to F( (t), u(t), t) for t [tk , tk+1 ) Both k and Qdk x will therefore be time varying A method for computing k and Qdk online is discussed in Section 7252 Several examples and applications are discussed in subsequent chapters In navigation applications, the F matrix is typically not asymptotically stable Therefore, without aiding, the trajectory perturbation typically diverges with time This is exempli ed in Sections 493 and 494 where the divergence between aiding measurements is clearly shown For the linearized error dynamics to remain accurate, either the time interval of interest should be short or some aiding mechanism should be used to ensure that x(t) x(t) remains small 51051 Linearized Kalman Filter

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In certain applications, orbiting satellites or interplanetary travel, the ve hicle is expected to closely follow a predetermined trajectory x(t) that is computed in advance In such applications, where x(t) is a predetermined trajectory, the approximate optimal estimation equations are referred to as a Linearized Kalman Filter In this case, the on-line calculations can be signi cantly simpli ed since the on-line measurements do not a ect the calculation of F, , H, P, Qd, or K Table 57 organizes the calculations of Table 56 into on-line and o -line calculations The estimate of the lter can be interpreted as the error between the actual and prespeci ed nominal trajectory A control law can be designed to attempt to drive this error toward zero 51052 Extended Kalman Filter

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In many applications, human driven vehicles or autonomous vehicles with data-reactive missions, the vehicle trajectory cannot be accurately predicted during the design of the navigation system See Part II for several applications of this approach In such applications, the nominal trajectory is de ned to be equal to the estimated trajectory, x(t) = x(t) In this case, the state estimation gain vector K (which is a function of x) will be a function of the stochastic process x(t) Therefore, the gain vector is a stochastic process As long as the state is observable from the measurements and the measurements are

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O Neil has advice for investors who are using market indicators: Most investors like to follow different indicators, when in reality there is only one general market indicator we have found, cycle after cycle, to be dependable In the stock market, three out of four stocks will decline when the major market indicators correct These are the indicators we use to tell us when the general market is about to go south They measure the general market more precisely Before he discovered the one market indicator that he believed was the most reliable, he analyzed all the different indicators known at that time O Neil notes that they created the method many years ago after testing and throwing out virtually all other technical market indicators, which can work from time to time, but not on a consistent basis What he learned works best is following a daily chart that shows price and volume, day by day, of the major market indexes A market top starts when one index runs into a four- or five-week period where there are five or more days of volume distribution, or selling, says O Neil This may sound new to most investors, but it is virtually impossible for the general market to go into an intermediate-term or bear market correction without this happening The indicator he uses primarily follows the actions of large institutions He explains it in more detail: We only count a distribution day when one of these indexes closes down two-tenths of a percent or more from the day before on heavier volume, he says, and you can see an accumulation of five different days over a four- or five-week period This historically has resulted in the market rolling over into a correction His indicator is 80 to 90 percent accurate, O Neil maintains It will take some time to learn what you re looking for, and the.

CHAPTER 5 OPTIMAL STATE ESTIMATION O -line Calculations x(t) = f ( (t), u(t), t) x yk = h( k , k) x f F( (t), u(t), t) = x x= x x

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