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The main topic of this chapter is the derivation and study of the topic of optimal state estimation Sections 36 and 48 have already introduced the concept of state estimation In those sections the state estimation gain was represented by L The gain L could, for example, be selected by pole placement methods Given a stochastic model and a stabilizing gain L, the system performance could be evaluated by the methods discussed in Section 49 This chapter presents the state estimation problem from a di erent perspective: given a stochastic model, can the state estimation gain be selected as a time-varying sequence that optimizes some measure of the performance of the system The state estimation algorithm that uses this optimal timevarying gain sequence will be referred to as the Kalman lter The state estimation problem is reviewed in Section 51.



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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.

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Using the mean-squarederror of the state covariance sequence as the optimization criteria, we derive the Kalman lter by two distinct methods Section 52 sets up an optimization problem and selects the optimal state estimation gain Kk at each time step For an alternative perspective, Section 53 starts with the problem of weighted least squares, progresses through recursive weighted least squares, and nally uses time propagation of the state and covariance matrix to derive the Kalman lter gain sequence Kk Section 54 summarizes the derivations of the Kalman lter algorithm and presents di erent approaches to computing the variance of the state estimation error after a measurement correction Section 55 discusses useful properties of the Kalman lter Section 56 discusses various issues that should be understood for e cient and successful Kalman lter implementations Section 59 discusses two numeric issues important for Kalman lter implementations.

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[ROW/COL] is an optional input parameter. The RunningCount function can be applied across either rows or columns. [INCLUDEEMPTY] is an optional parameter. The IncludeEmpty parameter tells the RunningCount function to include null [report_variable] values in the count. The [reset_dimension_variables] are optional input parameters. Providing a dimension_variable or a list of dimension_variables tells the RunningCount function to reset the count back to 0 whenever the dimension_variable(s) change values. Examples:

Core Issue: Take pleasure in other people s positive qualities and accomplishments. A counterintuitive way to do this is to first take genuine pleasure in your own positive attributes and accomplishments. Do not use any caveats or buts, such as I m smart, but he s smarter or I m empathic, but I spend too much time thinking about myself. Simply enjoy who you are. Once you can do this, allow yourself to appreciate other people s qualities and achievements. Each day, think positively about yourself, then select another person to think positively about without making any comparisons to yourself. Expansion Through Wings and Arrows THREE WING Stay in charge of yourself. You can learn how to keep your feelings from interfering with the work you need to do by saying to yourself: I am feeling very emotional, but I am not going to allow my feelings to take over. What tasks need my attention right now Each time you feel your emotions taking over, ask yourself the question again. Value your mental clarity. Although you value your emotional reactions and experiences, you likely place less emphasis on your mind, even if you have highly developed analytical skills. There is, however, a unique wisdom in both your mind and your heart (as well as in your body). Use your mental agility for planning for example, the next time you need to make a decision, develop a methodical process for how you will make the decision and design an implementation

The process_ID is locally significant and is used to differentiate between OSPF processes running on the same router. Your router might be a boundary router between two OSPF autonomous systems, and to differentiate them on your router, you ll give them unique process IDs. Note that these numbers do not need to match between different routers and that they have nothing to do with autonomous system numbers. (The process of configuring two OSPF processes on the same router is beyond the scope of this book.)

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