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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: crystal report barcode code 128 How to Create a Code 128 Barcode in Crystal Reports using the ...
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