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P SD = 10 and var( k ) = 10 The measurements are available at a 10 Hz rate The performance of the steady-state lter (dashed-dotted) relative to the performance of the Kalman lter with time-varying gains (solid) is displayed in Figure 56 The two estimators have the same performance until the instant of the rst measurement at t = 10 s The time-varying Kalman lter makes a much larger correction initially due to the large initial error covariance During the initial transient, the relative performance 2 2 di erence is large with 1 + 2 always smaller for the Kalman lter than for the steady state lter Since both lters are stable and both lters use the same gains in steady-state, the steady-state performance of the steady state lter is identical to that of the full Kalman lter implementation Note that the steady state lter approach will fail to apply directly if either the Kalman gains are zero in steady state or do not approach constant values as t
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your research on stocks with the best ratings, she says Our researchers have found that stocks that outperform during a poor market are frequently the ones that make big price gains when the market s rally resumes In a downturn, Stalter suggests creating a watch list of fundamentally and technically strong stocks that could outperform when the market turns around When the market does turn around, be very choosy about which stocks to buy, and be ready to cut them quickly if market action is sputtering, she cautions Once again, Stalter says, the keys to determining if you re in an uptrend or downtrend are price and volume Professional investors account for the bulk of market action, she points out Institutions drive market direction Stalter has some final advice: It takes a bit of time to master those rules, but it s worth it And it pays to control emotions like fear and greed, so that s where having a set of buy and sell rules helps That way, you can operate with a logical plan you adhere to in every market scenario It s a whole lot better than relying on your own emotions, or even worse, listening to tips and opinions that you hear on TV
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The previous sections have concentrated on the design and analysis of optimal (and suboptimal) estimation algorithms for linear systems In many
Kate Stalter is a markets writer for Investor s Business Daily and host of the Market Wrap video at Investorscom Since 2001, she has edited and written for IBD columns The Real Most Active, Stocks in the News, and Investor s Corner Stalter has cohosted Webcasts with TDAmeritrade and regularly presents at IBD investing seminars nationwide She is an in-demand commentator on national radio programs She received her MBA from the Kellogg School of Management at Northwestern University
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Figure 56: Standard deviation of the estimation errors of steady state lter (dashed-dotted) relative to time varying Kalman lter (solid) The error 2 2 variance of x1 is 1 The error variance of x2 is 2 navigation applications either the system dynamic equations or the measurement equations are not linear For example, the GPS range measurement (592) r = (xs xr )2 + (ys yr )2 + (zs zr )2 is nonlinear in the receiver antenna position (xr , yr , zr ) It is therefore necessary to consider extensions of the linear optimal estimation equations to nonlinear systems This is the objective of the present section The section mainly considers continuous-time systems with discrete measurements, as they are most pertinent to navigation applications The presentation herein is limited to two of the traditionally most common nonlinear ltering techniques referred to as the Linearized Kalman lter and the Extended Kalman lter Other nonlinear optimal estimation approaches such as particle lters or unscented Kalman lters are discussed for example in [121] The results of this section will be used extensively in the applications in Part II of this text Consider the nonlinear system with dynamics and measurement described by x(t) = f (x(t), u(t), t) + (t) y(t) = h(x(t), t) + (t) (593) (594)
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Although this model is restrictive in assuming additive uncorrelated white noise processes and , the model is typical of the systems found in navigation applications The signal u(t) is a known signal and f and h are known smooth functions De ne a reference trajectory x satisfying x(t) = f ( (t), u(t), t) x y(t) = h( (t), t) x (595) (596)
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