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In Part Four, which should be extremely useful, you ll find out where to go for help if you want to learn more about indicators For now, many of you will find the next piece in 11, What to Do in a Market Emergency, required reading

2 where wk is a zero mean, white Gaussian process with variance w The parameter a = exp( 1/ ) where is the correlation time in seconds The 2 positive parameters w and a are related by w = 1 a2

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1 Denote the variance of nk as Pn (k) = var(nk ) Con rm that propagation of the variance Pn , using the model given by eqn (5112), will result in Pn = 10 in steady-state

what to do before the next market emergency In addition, you ll find a list of important resources, including a glossary, an indicator cheat sheet, useful charts, and where to go if you want to take indicators to a higher level When time is not on your side, these chapters could be portfolio savers

2 Design a (complementary form) Kalman lter for the system with state xk = [B, nk ] and measurement rk = zk yk Show that the state transition model for the state has = 1 0 0 a , Qd = 0 0 0 2 w ,

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traded using market indicators, I d like to share a few observations with you You may or may not agree with my conclusions, but keep in mind these are my opinions I ll begin by giving you a few suggestions in case you are confronted with a market emergency

2 0 0

with 0 near in nity Multiply out eqn (561), then take the limit as 0 approaches in nity to show that K1 = [1, 0] Implement the Kalman lter to compute Kk , P+ , and P For k k = 60s, run the simulation and answer the following questions How many iterations are require for the standard deviation of the estimate of B to be less that 01 In steady-state, what is the Kalman gain K 3 Refer back to Example 54 where the correlation of nk was ignored For that example, answer the following questions How many iterations are require for the standard deviation of the estimate of B to be less that 01 In steady-state, what is the Kalman gain K Exercise 512 For the system describe in Exercise 511, design a SchmidtKalman lter that estimates B, but not nk

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If you are reading this for entertainment and it s not a market emergency, take steps now to prepare for worst-case scenarios Obviously, the best time to make financial decisions is when you want to, not when you have to In the stock market, anything can happen, so it s best to prepare before you are caught by surprise If, however, this is a real market emergency, and you re nervous or scared, read the next section It may help keep you calm

6

It never fails When you are feeling the most emotional, confused, and vulnerable, you ll read a series of articles about how the market will completely collapse tomorrow Perhaps you ll get a call from a panicked friend who tells you to sell everything now!

This chapter presents methods to evaluate the performance of a state estimator The state estimator is assumed to produce an unbiased estimate (ie E x(t) = E x(t) ) and performance is quanti ed by the state estimate error covariance matrix 5 assumed that the estimator design model was an exact model of the actual system This assumption is optimistic In the design process, as discussed in Section 510, a series of design tradeo s may be considered that result in the design model being a reduced order approximation to the truth model (ie, best available linear model for the system) Due to such tradeo s and the fact that any mathematical model is only an approximation to the actual physical system, the estimator design model will not exactly match the real system Sections 61 and 62 are concerned with methods to study estimator performance under these more realistic conditions Section 63 presents methods that allow the designer to consider each source of system error separately to determine its contribution to the overall error budget

Figure 22: Left Exaggerated ellipse depicting the di erence between geodetic and geocentric latitude Right Depiction of a local frame-ofreference

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This section de nes various frames-of-reference that are commonly used in navigation system applications

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