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Course by: Laurence Riddle. E-mail the author

# White Gaussian Noise

Module by: Don Johnson. E-mail the author

By far the easiest detection problem to solve occurs when the noise vector consists of statistically independent, identically distributed, Gaussian random variables. In this book, a white sequence consists of statistically independent random variables. The white sequence's mean is usually taken to be zero 1 and each component's variance is σ2 σ 2 . The equal-variance assumption implies the noise characteristics are unchanging throughout the entire set of observations. The probability density of the zero-mean noise vector evaluated at r s i r s i equals that of Gaussian random vector having independent components ( K=σ2I K σ 2 I ) with mean s i s i . p n r s i =12πσ2L2e(12σ2(r s i )T(r s i )) p n r s i 1 2 σ 2 L 2 1 2 σ 2 r s i r s i The resulting detection problem is similar to the Gaussian example examined so frequently in the hypothesis testing sections, with the distinction here being a non-zero mean under both models. The logarithm of the likelihood ratio becomes (r s 0 )T(r s 0 )(r s 1 )T(r s 1 ) 1 0 2σ2lnη r s 0 r s 0 r s 1 r s 1 1 0 2 σ 2 η and the usual simplifications yield in rT s 1 s 1 T s 1 2(rT s 0 s 0 T s 0 2) 1 0 σ2lnη r s 1 s 1 s 1 2 r s 0 s 0 s 0 2 1 0 σ 2 η The quantities in parentheses express the signal processing operations for each model. If more than two signals were assumed possible, quantities such as these would need to be computed for each signal and the largest selected. This decision rule is optimum for the additive, white Gaussian noise problem.

Each term in the computations for the optimum detector has a signal processing interpretation. When expanded, the term s i T s i s i s i equals l =0L1 s i 2l l 0 L 1 s i l 2 , which is the signal energy E i E i . The remaining term - rT s i r s i - is the only one involving the observations and hence constitutes the sufficient statistic ϒ i r ϒ i r for the additive white Gaussian noise detection problem. ϒ i r=rT s i ϒ i r r s i An abstract, but physically relevant, interpretation of this important quantity comes from the theory of linear vector spaces. There, the quantity rT s i r s i would be termed the dot product between rr and s i s i or the projection of rr onto s i s i . By employing the Schwarz inequality, the largest value of this quantity occurs when these vectors are proportional to each other. Thus, a dot product computation measures how much alike two vectors are: they are completely alike when they are parallel (proportional) and completely dissimilar when orthogonal (the dot product is zero). More precisely, the dot product removes those components from the observations which are orthogonal to the signal. The dot product thereby generalizes the familiar notion of filtering a signal contaminated by broadband noise. In filtering, the signal-to-noise ratio of a bandlimited signal can be drastically improved by lowpass filtering; the output would consist only of the signal and "in-band" noise. The dot product serves a similar role, ideally removing those "out-of-band" components (the orthogonal ones) and retaining the "in-band" ones (those parallel to the signal).

Expanding the dot product, rT s i = l =0L1rl s i l r s i l 0 L 1 r l s i l another signal processing interpretation emerges. The dot product now describes a finite impulse response (FIR) filtering operation evaluated at a specific index. To demonstrate this interpretation, let hl h l be the unit-sample response of a linear, shift-invariant filter where hl=0 h l 0 for l<0 l 0 and lL l L . Letting rl r l be the filter's input sequence, the convolution sum expresses the output. rk*hk= l =k(L1)krlhkl r k h k l k L 1 k r l h k l Letting k=L1 k L 1 , the index at which the unit-sample response's last value overlaps the input's value at the origin, we have rk*hk| k =L1= l =0L1rlhL1l k L 1 r k h k l 0 L 1 r l h L 1 l If we set the unit-sample response equal to the index-reversed, then delayed signal hl= s i L1l h l s i L 1 l , we have rk* s i L1k| k =L1= l =0L1rl s i l k L 1 r k s i L 1 k l 0 L 1 r l s i l which equals the observation-dependent component of the optimal detector's sufficient statistic. Figure 1 depicts these computations graphically.

The sufficient statistic for the i th i th signal is thus expressed in signal processing notation as rk* s i L1k| k =L1 E i 2 k L 1 r k s i L 1 k E i 2 . The filtering term is called a matched filter because the observations are passed through a filter whose unit-sample response "matches" that of the signal being sought. We sample the matched filter's output at the precise moment when all of the observations fall within the filter's memory and then adjust this value by half the signal energy. The adjusted values for the two assumed signals are subtracted and compared to a threshold.

To compute the performance probabilities, the expressions should be simplified in the ways discussed in the hypothesis testing sections. As the energy terms are known a priori they can be incorporated into the threshold with the result l =0L1rl( s 1 l s 0 l) 1 0 σ2lnη+ E 1 E 0 2 l 0 L 1 r l s 1 l s 0 l 1 0 σ 2 η E 1 E 0 2 The left term constitutes the sufficient statistic for the binary detection problem. Because the additive noise is presumed Gaussian, the sufficient statistic is a Gaussian random variable no matter which model is assumed. Under i i , the specifics of this probability distribution are l =0L1rl( s 1 l s 0 l)𝒩 s i l( s 1 l s 0 l)σ2 s 1 l s 0 l2 l 0 L 1 r l s 1 l s 0 l s i l s 1 l s 0 l σ 2 s 1 l s 0 l 2 The false-alarm probability is given by P F =Qσ2lnη+ E 1 E 0 2 s 0 l( s 1 l s 0 l)σ s 1 l s 0 l212 P F Q σ 2 η E 1 E 0 2 s 0 l s 1 l s 0 l σ s 1 l s 0 l 2 1 2 The signal-related terms in the numerator of this expression can be manipulated with the false-alarm probability (and the detection probability) for the optimal white Gaussian noise detector succinctly expressed by P F =Qlnη+12σ2 s 1 l s 0 l21σ s 1 l s 0 l212 P F Q η 1 2 σ 2 s 1 l s 0 l 2 1 σ s 1 l s 0 l 2 1 2 P F =Qlnη12σ2 s 1 l s 0 l21σ s 1 l s 0 l212 P F Q η 1 2 σ 2 s 1 l s 0 l 2 1 σ s 1 l s 0 l 2 1 2

Note that the only signal-related quantity affecting this performance probability (and all of the others) is the ratio of energy in the difference signal to the noise variance. The larger this ratio, the better (smaller) the performance probabilities become. Note that the details of the signal waveforms do not greatly affect the energy of the difference signal. For example, consider the case where the two signal energies are equal ( E 0 = E 1 =E E 0 E 1 E ); the energy of the difference signal is given by 2E2 s 0 l s 1 l 2 E 2 s 0 l s 1 l . The largest value of this energy occurs when the signals are negatives of each other, with the difference-signal energy equaling 4E 4 E . Thus, equal-energy but opposite-signed signals such as sine waves, square-waves, Bessel functions, etc. all yield exactly the same performance levels. The essential signal properties that do yield good performance values are elucidated by an alternate interpretation. The term s 1 l s 0 l2 s 1 l s 0 l 2 equals s 1 s 0 2 s 1 s 0 2 , the L2 L 2 norm of the difference signal. Geometrically, the difference-signal energy is the same quantity as the square of the Euclidean distance between the two signals. In these terms, a larger distance between the two signals will mean better performance.

## Example 1: Detection, Gaussian example

A common detection problem in array processing is to determine whether a signal is present ( 1 1 ) or not ( 0 0 ) in the array output. In this case, s 0 l=0 s 0 l 0 The optimal detector relies on filtering the array output with a matched filter having an impulse response based on the assumed signal. Letting the signal under 1 1 be denoted simply by sl s l , the optimal detector consists of rl*sL1l| l =L1E2 1 0 σ2lnη l L 1 r l s L 1 l E 2 1 0 σ 2 η or rl*sL1l| l =L1 1 0 γ l L 1 r l s L 1 l 1 0 γ The false-alarm and detection probabilities are given by P F =QγE12σ P F Q γ E 1 2 σ P D =QQ-1 P F Eσ P D Q Q P F E σ Figure 2 displays the probability of detection as a function of the signal-to-noise ratio Eσ2 E σ 2 for several values of false-alarm probability. Given an estimate of the expected signal-to-noise ratio, these curves can be used to assess the trade-off between the false-alarm and detection probabilities.

The important parameter determining detector performance derived in this example is the signal-to-noise ratio Eσ2 E σ 2 : the larger it is, the smaller the false-alarm probability is (generally speaking). Signal-to-noise ratios can be measured in many different ways. For example, one measure might be the ratio of the rms signal amplitude to the rms noise amplitude. Note that the important one for the detection problem is much different. The signal portion is the sum of the squared signal values over the entire set of observed values - the signal energy; the noise portion is the variance of each noise component - the noise power. Thus, energy can be increased in two ways that increase the signal-to-noise ratio: the signal can be made larger or the observations can be extended to encompass a larger number of values.

To illustrate this point, two signals having the same energy are shown in Figure 3. When these signals are shown in the presence of additive noise, the signal is visible on the left because its amplitude is larger; the one on the right is much more difficult to discern. The instantaneous signal-to-noise ratio-the ratio of signal amplitude to average noise amplitude - is the important visual cue. However, the kind of signal-to-noise ratio that determines detection performance belies the eye. The matched filter outputs have similar maximal values, indicating that total signal energy rather than amplitude determines the performance of a matched filter detector.

## Validity of the White Noise Assumption

The optimal detection paradigm for the additive, white Gaussian noise problem has a relatively simple solution: construct FIR filters whose unit-sample responses are related to the presumed signals and compare the filtered outputs with a threshold. We may well wonder which assumptions made in this problem are most questionable in "real-world" applications. noise is additive in most cases. In many situation, the additive noise present in observed data is Gaussian. Because of the Central Limit Theorem, if numerous noise sources impinge on a measuring device, their superposition will be Gaussian to a great extent. As we know from the discussion on the Central Limit Theorem, glibly appealing to the Central Limit Theorem is not without hazards; the non-Gaussian detection problem will be discussed in some detail later. Interestingly, the weakest assumption is the "whiteness" of the noise. Note that the observation sequence is obtained as a result of sampling the sensor outputs. Assuming white noise samples does not mean that the continuous-time noise was white. White noise in continuous time has infinite variance and cannot be sampled; discrete-time white noise has a finite variance with a constant power spectrum. The Sampling Theorem suggests that a signal is represented accurately by its samples only if we choose a sampling frequency commensurate with the signal's bandwidth. One should note that fidelity of representation does not mean that the sample values are independent. In most cases, satisfying the Sampling Theorem means that the samples are correlated. As shown in Sampling and Random Sequences, the correlation function of sampled noise equals samples of the original correlation function. For the sampled noise to be white, En l 1 Tn l 2 T=0 n l 1 T n l 2 T 0 for l 1 l 2 l 1 l 2 : the samples of the correlation function at locations other than the origin must all be zero. While some correlation functions have this property, many examples satisfy the sampling theorem but do not yield uncorrelated samples. In many practical situations, undersampling the noise will reduce inter-sample correlation. Thus, we obtain uncorrelated samples either by deliberately undersampling, which wastes signal energy, or by imposing anti-aliasing filters that have a bandwidth larger than the signal and sampling at the signal's Nyquist rate. Since the noise power spectrum usually extends to higher frequencies than the signal, this intentional undersampling can result in larger noise variance. in either case, by trying to make the problem at hand match the solution, we are actually reducing performance! We need a direct approach to attacking the correlated noise issue that arises in virtually all sampled-data detection problems rather than trying to work around it.

## Footnotes

1. The zero-mean assumption is realistic for the detection problem. If the mean were non-zero, simply subtracting it from the observed sequence results in a zero-mean noise component.

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