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The statistical quantities de ned previously for random variables become slightly more complicated when applied to stochastic processes, as they may depend on the parameter t or k For continuous-time processes, the de nitions are listed below Note that these de nitions are equally valid whether the parameter t represents a continuous or a discrete-time variable The cross correlation function between two random processes is de ned by (429) Rvw (t1 , t2 ) = cor (v(t1 ), w(t2 )) = E v(t1 )w(t2 ) The autocorrelation function of a random processes is de ned by Rv (t1 , t2 ) = cor (v(t1 ), v(t2 )) = E v(t1 )v(t2 ) (430)

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The autocorrelation function quanti es the similarity of the random process to itself at two di erent times The cross covariance function between two random processes is de ned by cov (v(t1 ), w(t2 )) = E v(t1 )w(t2 ) E v(t1 ) E w(t2 ) (431)

The autocovariance function of a random processes is de ned by cov (v(t1 ), v(t2 )) = E v(t1 )v(t2 ) E v(t1 ) E v(t2 ) (432)

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23 In a personal computer, the internal mass-storage medium is nearly always (a) a CD (compact disc) in some form (b) a diskette drive (c) a tape drive (d) read-only memory (ROM) (e) none of the above 24 A device to which signals can be sent asking for data, and that responds with the requested information by means of radio or a wireless network, is known as a (a) transceiver (b) transponder (c) repeater (d) duplexer (e) signal reflector 25 Suppose you want to use a transformer to obtain exactly 30 V rms ac from an electrical outlet that supplies exactly 120 V rms ac The primary-to-secondary turns ratio of the transformer should be (a) exactly 1:16 (b) exactly 1:4 (c) variable (d) exactly 4:1 (e) exactly 16:1 26 Fill in the blank in the following statement to make it true: Two problems can occur with the type of intrusion alarm First, the sun, suddenly emerging from clouds or from shadow, might shine on the sensor and trigger the device when there is no threat Second, someone with malicious intent, wearing winter clothing and entering on a cold day, might fail to set off the alarm in the presence of a real threat (a) IR radiation detector (b) electromagnetic detector (c) ultrasonic detector (d) ultraviolet detector (e) fluxgate detector 27 Suppose that five resistors, each one having the same ohmic value as all the others, are connected in series, and the entire combination is connected to a 12-V battery How does the voltage across any one of the resistors compare with the battery voltage, assuming no other loads are connected to the circuit (a) The voltage across any one of the resistors is 5 times the battery voltage (b) The voltage across any one of the resistors is the same as the battery voltage (c) The voltage across any one of the resistors is 1 5 of the battery voltage (d) The voltage across any one of the resistors is 1 25 of the battery voltage (e) It is impossible to answer this without more information.

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The above de nitions will be critically important in the analysis of subsequent chapters For example, the Kalman lter will propagate certain error covariance matrices through time as a means to quantify the relative accuracy of various pieces of information that are to be combined There are two forms of stationary random processes that will be used in this text De nition 41 The random process w(t) is stationary if its distribution is independent of time (ie, pw (W, t) = pw (W)) If a random process is stationary, then its expected value will be time invariant and both its autocorrelation function and its autocovariance function will only depend on the time di erence = t1 t2 (ie, Rv (t1 , t2 ) = Rv ( )) Often this strict sense of stationarity is too restrictive A relaxed sense of stationarity is as follows De nition 42 The random process w(t) is wide sense stationary (WSS) if the mean and variance of the process are independent of time Since a Gaussian random process is completely de ned by its rst two moments, a Gaussian process that is wide sense stationary is also stationary A wide sense stationary process must have a constant mean, and its correlation and covariance can only depend on the time di erence between the occurrence of the two random variables, ie cor (w(t1 ), w(t2 )) = Rww ( ) where = t1 t2 The Power Spectral Density (PSD) of a WSS random process w(t) is the Fourier transform of the autocorrelation function:

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