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The topic of stochastic processes is very important to the study of navigation system design as it is the foundation on which we build our ability to quantitatively analyze the propagation of uncertainty through the navigation system as a function of time A thorough presentation of the related topics of statistics, probability theory, random variables, and stochastic processes is well beyond the scope of this text Several excellent texts are mentioned at the conclusion of this chapter Instead, this chapter focuses on topics selected for their relevance to the design and analysis of navigation systems The following examples are intended to motivate the topics

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Free source code and tutorials for Software developers and Architects.; Updated: 14 Jul 2012.

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1 Type wwwstockchartscom in your Web address line (or open any chart program) 2 On the right side of the screen, type $SPX, a symbol for the S&P 500 Index Note: If you want, you can also choose $INDU (Dow Jones Industrial Average) or $COMPQ (Nasdaq Composite) 3 When the chart appears, you should see the 50-day and 200-day simple moving averages on the screen along with the market index, $SPX

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Figure 41: Oscilloscope image of a sinusoidal input discussed in the balance of this chapter Example 41 Most engineers have used an oscilloscope at some point in their careers An example image from an oscilloscope screen is shown in Figure 41 This image is created by directly connecting the output of a sinusoidal source to the oscilloscope input The image clearly shows a noisy version of a sinusoidal signal This gure is included to clearly exemplify the concept of measurement noise The noise is the result of several complex physical processes, eg, thermal noise in the probes and electronic parts Quite often it is accurate to describe a measurement in the form y(t) = Sx(t) + (t), where y represents the measurement, x represents the actual value of some physical property, S is a scale factor, and represents measurement noise Typically, the manufacturer of the instrument is careful to design the instrument such that the scale factor S is constant and accurately known and that the measurement noise is not related to the signal x In addition, the manufacturer will often provide the purchaser with the quantitative description of suitable for further analysis To be able to communicate with sensor manufacturers and other navigation engineers and to be able to perform quantitative analysis, the analyst must be familiar with stochastic process de nitions and methods

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Figure 42: Angular rate (top) and integrated angle (bottom) for Example 42 Example 42 This example illustrates the types of processes and signals that occur in navigation systems and some possible questions that are of interest via stochastic analysis methods An angular rate measurement r(t) from a gyro is shown as the top graph of Figure 42 The gyro is mounted in such a way that it can only rotate about a single axis that corresponds to the gyro sensitive axis The analyst would like to understand the nature of the error between the true angle and the angle estimate computed by integration of the gyro output To begin, we determine a model for the sensor signal The model can either be determined by laboratory experiments performed by the analyst or, more typically, provided by the sensor manufacturer The methodology for estimating the error model is discussed in Section 492 For this example, assume that the angular rate sensor is accurately modeled as r(t) = r(t) + bg (t) + (t), (41)

$SPX (Weekly) 109531 MA(50) 109051 MA(200) 121731

where r represents the real rotation rate around the sensitive axis of the gyro, bg represents a very slowly changing component often referred to as a bias, and corresponds to measurement error that has a distinct value at each measurement instance Although we would prefer to integrate r(t) to compute , we only have available the signal r Our integration of r yields

1500 1400 1300 121731 1200 1100 109531 1000

the angle estimate We will assume that (0) = 0 A model for the error can be developed as between and (t) =

(42) (43) (44) (45)

900 800 700

= = (t) (t) =

4 On your computer screen, the 50-day line is blue, and the 200-day line is red 5 The chart should look something like Figure 31

In this set of equations, eqn (42) shows the actual computation of as the integral of the sensor output r: (t) = r(t)

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To put it simply, no. There is no way to convert it to a DOC file using iTextSharp . It only supports the reading and generating of PDF files.

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