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same as Figure 76 after accounting for the constraint of eqn (725) The system in the right portion of Figure 77 is a reorganization of the block diagram In the reorganized implementation, the input to the lter G2 (s) is the signal n2 n1 which is a stationary random signal with known spectral content Therefore, we have the tools available (eg, the Wiener lter) to design the optimal estimate of n1 based on the input signal n2 n1 The output of the lter n1 can be subtracted from y1 to produce the estimate s(t) Note that this s(t) is suboptimal It is the optimal estimate of s(t) subject to the complementary lter constraint, but there may be lters with better performance that operate on y1 (t) and y2 (t) We have used the complementary lter constraint to convert the original problem into a new problem for which our available design and analysis methods are appropriate The Wiener lter design approach was not applicable to the original problem because no stochastic model was available for the signal s The complementary lter approach is especially useful in applications where the sensors have complementary spectral characteristics For example, if n1 has its spectral content predominantly in the low frequency range while n2 has its spectral content predominantly in the high frequency range, then the lter G2 (s) would be some form of low pass lter

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sider it If it turns out your initial assumptions do not coincide with the statistics, then you have to reassess the situation Otherwise, you are fooling yourself It s not that easy to correctly interpret economic indicators No single indicator should be viewed as a crystal ball of where things are going, Baumohl explains You really have to look at a variety of different indicators The goal is to find a pattern that emerges You want them to corroborate each other When several indicators point in the same direction, you can say with increasing confidence where the economy is headed and how financial assets should best be allocated Although reading economic indicators is a very intuitive process, he says, eventually the indicators line up together The only time you will get a confusing mixture of both positive and negative signals is when the economy approaches an inflection point An inflection point occurs either at the lowest point of the business cycle or the peak of the cycle When the economy is expanding, he notes, there s a preponderance of positive numbers In the middle of a recession, there is a preponderance of negative numbers But when you get to the turning points, that s when you will often get a confusing mixture of both strong and weak economic signals Perhaps most important, you have to be nimble Let s face it, Baumohl says A lot of what is going on in the world is unpredictable There are so many economic and noneconomic events taking place every day, week, and month around the world that you can t stick to any rigid rules or guidelines That s why economic forecasters and investors have to be agile and use good judgment

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The previous section presented the complementary lter from a frequency domain perspective Because the Kalman lter is a time varying lter, the transfer function perspective is not directly applicable; however, the complementary lter idea does extend to Kalman lter applications as discussed in Section 51053 and the ideas related to frequency separation between the various error and noise sources is still important The direct application of the Kalman lter to the navigation problem of Section 71 is considered in Section 74 The problematic issue in Section 74 will be the signal a(t) for which a time invariant stochastic model does not exist Alternatively, the approach of Section 71 addresses this issue by using the Kalman lter in a complementary lter format that eliminates a(t) from the estimation problem Note that the Kalman lter is designed for

74 AN ALTERNATIVE APPROACH the error dynamics of eqn (715) aided by the residual measurement yk = = = yk y k pk + by (k) + 2 H x + 2

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For a semiconductor material to have the properties necessary in order to function as electronic components, impurities are usually added The impurities cause the material to conduct currents in certain ways The addition of an impurity to a semiconductor is called doping Sometimes the impurity is called a dopant

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