The general statement of the Cauchy-Schwarz inequality mirrors the intuition for standard Euclidean space. Let VV be an inner product space over the field of complex numbers CC with inner product 〈·,·〉〈·,·〉. For every pair of vectors x,y∈Vx,y∈V the inequality

holds. Furthermore, the equality

holds if and only if y=axy=ax for some a∈Ca∈C. That is, equality holds if and only if xx and yy are linearly dependent.

Let VV be a vector space over the real or complex field FF, and let x,y∈Vx,y∈V be given. In order to prove the Cauchy-Schwarz inequality, it will first be proven that |〈x,y〉|2=〈x,x〉〈y,y〉|〈x,y〉|2=〈x,x〉〈y,y〉 if y=axy=ax for some a∈Fa∈F. It will then be shown that |〈x,y〉|2<〈x,x〉〈y,y〉|〈x,y〉|2<〈x,x〉〈y,y〉 if y≠axy≠ax for all a∈Fa∈F.

Consider the case in which y=axy=ax for some a∈Fa∈F. From the properties of inner products, it is clear that

Hence, it follows that

Similarly, it is clear that

Thus, it is proven that |〈x,y〉|2=〈x,x〉〈y,y〉|〈x,y〉|2=〈x,x〉〈y,y〉 if x=ayx=ay for some a∈Fa∈F.

Next, consider the case in which y≠axy≠ax for all a∈Fa∈F, which implies that y≠0y≠0 so 〈y,y〉≠0〈y,y〉≠0. Thus, it follows by the properties of inner products that, for all a∈Fa∈F, 〈x-ay,x-ay〉>0.〈x-ay,x-ay〉>0. This can be expanded using the properties of inner products to the expression

Choosing a=〈x,y〉〈y,y〉a=〈x,y〉〈y,y〉,

Hence, it follows that 〈x,x〉-〈x,y〉〈y,x〉〈y,y〉>0.〈x,x〉-〈x,y〉〈y,x〉〈y,y〉>0. Consequently, 〈x,x〉〈y,y〉-〈x,y〉〈x,y¯〉>0.〈x,x〉〈y,y〉-〈x,y〉〈x,y¯〉>0. Thus, it can be concluded that |〈x,y〉|2<〈x,x〉〈y,y〉|〈x,y〉|2<〈x,x〉〈y,y〉 if y≠axy≠ax for all a∈Fa∈F.

Therefore, the inequality

holds for all x,y∈Vx,y∈V, and equality

holds if and only if y=axy=ax for some a∈Fa∈F.

Consider the maximization of x||x||,y||y||x||x||,y||y|| where the norm ||·||=〈·,·〉||·||=〈·,·〉 is induced by the inner product. By the Cauchy-Schwarz inequality, we know that x||x||,y||y||2≤1x||x||,y||y||2≤1 and that x||x||,y||y||2=1x||x||,y||y||2=1 if and only if y||y||=ax||x||y||y||=ax||x|| for some a∈Ca∈C. Hence, x||x||,y||y||x||x||,y||y|| attains a maximum where y||y||=ax||x||y||y||=ax||x|| for some a∈Ca∈C. Thus, collecting the scalar variables, x||x||,y||y||x||x||,y||y|| attains a maximum where y=axy=ax. This result will be particulaly useful in developing the matched filter detector techniques.

A great many applications in signal processing, image processing, and beyond involve determining the presence and location of a target signal within some other signal. A radar system, for example, searches for copies of a transmitted radar pulse in order to determine the presence of and distance to reflective objects such as building or aircraft. A communication system searches for copies of waveforms representing digital 0s and 1s in order to receive a message.

As has already been shown, the expression x||x||,y||y||x||x||,y||y|| attains its upper bound, which is 1, when y=axy=ax for some scalar aa in a real or complex field. The lower bound, which is 0, is attained when xx and yy are orthogonal. In informal intuition, this means that the expression is maximized when the vectors xx and yy have the same shape or pattern and minimized when xx and yy are very different. A pair of vectors with similar but unequal shapes or patterns will produce relatively large value of the expression less than 1, and a pair of vectors with very different but not orthogonal shapes or patterns will produce relatively small values of the expression greater than 0. Thus, the above expression carries with it a notion of the degree to which two signals are “alike”, the magnitude of the normalized correlation between the signals in the case of the standard inner products.

This concept can be extremely useful. For instance consider a situation in which we wish to determine which signal, if any, from a set XX of signals most resembles a particular signal yy. In order to accomplish this, we might evaluate the above expression for every signal x∈Xx∈X, choosing the one that results in maxima provided that those maxima are above some threshold of “likeness”. This is the idea behind the matched filter detector, which compares a set of signals against a target signal using the above expression in order to determine which among them are most like the target signal. For a detailed treatment of the applications of the matched filter detector see the liked module.

The simplest variant of the matched filter detector scheme would be to find the member signal in a set XX of signals that most closely matches a target signal yy. Thus, for every x∈Xx∈X we wish to evaluate

in order to compare every member of XX to the target signal yy. Since the member of XX which most closely matches the target signal yy is desired, ultimately we wish to evaluate

Note that the target signal does not technically need to be normalized to produce a maximum, but gives the desirable property that m(x,y)m(x,y) is bounded to [0,1][0,1].

The element xm∈Xxm∈X that produces the maximum value of m(x,y)m(x,y) is not necessarily unique, so there may be more than one matching signal in XX. Additionally, the signal xm∈Xxm∈X producing the maximum value of m(x,y)m(x,y) may not produce a very large value of m(x,y)m(x,y) and thus not be very much like the target signal yy. Hence, another matched filter scheme might identify the argument producing the maximum but only above a certain threshold, returning no matching signals in XX if the maximum is below the threshold. There also may be a signal x∈Xx∈X that produces a large value of m(x,y)m(x,y) and thus has a high degree of “likeness” to yy but does not produce the maximum value of m(x,y)m(x,y). Thus, yet another matched filter scheme might identify all signals in XX producing local maxima that are above a certain threshold.

Example 1

For example, consider the target signal given in Figure 1 and the set of two signals given in Figure 2. By inspection, it is clear that the signal g2g2 is most like the target signal ff. However, to make that conclusion mathematically, we use the matched filter detector with the L2L2 inner product. If we were to actually make the necessary computations, we would first normalize each signal and then compute the necessary inner products in order to compare the signals in XX with the target signal ff. We would notice that the absolute value of the inner product for g2g2 with ff when normalized is greater than the absolute value of the inner product of g1g1 with ff when normalized, mathematically stated as

g 2 = argmax x ∈ { g 1 , g 2 } x | | x | | , f | | f | | . g 2 = argmax x ∈ { g 1 , g 2 } x | | x | | , f | | f | | .

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Figure 1: We wish to find a match for this target signal in the set of signals below.

Template Signal

Figure 2: We wish to find a match for the above target signal in this set of signals.

Candidate Signals
(a) (b)

A somewhat more involved matched filter detector scheme would involve attempting to match a target time limited signal y=fy=f to a set of of time shifted and windowed versions of a single signal X={wStg|t∈R}X={wStg|t∈R} indexed by RR. The windowing funtion is given by w(t)=u(t-t1)-u(t-t2)w(t)=u(t-t1)-u(t-t2) where [t1,t2][t1,t2] is the interval to which ff is time limited. This scheme could be used to find portions of gg that have the same shape as ff. If the absolute value of the inner product of the normalized versions of ff and wStgwStg is large, which is the absolute value of the normalized correlation for standard inner products, then gg has a high degree of “likeness” to ff on the interval to which ff is time limited but left shifted by tt. Of course, if ff is not time limited, it means that the entire signal has a high degree of “likeness” to ff left shifted by tt.

Thus, in order to determine the most likely locations of a signal with the same shape as the target signal ff in a signal gg we wish to compute

to provide the desired shift. Assuming the inner product space examined is L2(RL2(R (similar results hold for L2(R[a,b))L2(R[a,b)), l2(Z)l2(Z), and l2(Z[a,b))l2(Z[a,b))), this produces

Since ff and ww are time limited to the same interval

Making the subsitution h(t)=g(-t)¯h(t)=g(-t)¯,

Noting that this expression contains a convolution operation

where hh is the conjugate of the time reversed version of gg defined by h ( t ) = g ( - t ) ¯ . h ( t ) = g ( - t ) ¯ .

In the special case in which the target signal ff is not time limited, ww has unit value on the entire real line. Thus, the norm can be evaluated as ||wStg||=||Stg||=||g||=||h||||wStg||=||Stg||=||g||=||h||. Therefore, the function reduces to tm=argmaxt∈R(f*h)(t)||f||||h||tm=argmaxt∈R(f*h)(t)||f||||h|| where h(t)=g(-t)¯.h(t)=g(-t)¯. The function f☆g=(f*h)(t)||f||||h||f☆g=(f*h)(t)||f||||h|| is known as the normalized cross-correlation of ff and gg.

Hence, this matched filter scheme can be implemented as a convolution. Therefore, it may be expedient to implement it in the frequency domain. Similar results hold for the L2(R[a,b))L2(R[a,b)), l2(Z)l2(Z), and l2(Z[a,b])l2(Z[a,b]) spaces. It is especially useful to implement the l2(Z[a,b])l2(Z[a,b]) cases in the frequency domain as the power of the Fast Fourier Transform algorithm can be leveraged to quickly perform the computations in a computer program. In the L2(R[a,b))L2(R[a,b)) and l2(Z[a,b])l2(Z[a,b]) cases, care must be taken to zero pad the signal if wrap-around effects are not desired. Similar results also hold for spaces on higher dimensional intervals with the same inner products.

Of course, there is not necessarily exactly one instance of a target signal in a given signal. There could be one instance, more than one instance, or no instance of a target signal. Therefore, it is often more practical to identify all shifts corresponding to local maxima that are above a certain threshold.

Example 2

The signal in Figure 4 contains an instance of the template signal seen in Figure 3 beginning at time t=s1t=s1 as shown by the plot in Figure 5. Therefore,

s 1 = argmax t ∈ R f | | f | | , w S t g | | w S t g | | . s 1 = argmax t ∈ R f | | f | | , w S t g | | w S t g | | .

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Figure 3: This function shows tha pattern we are looking for in the signal below, which occurs at time t=s1t=s1.

Pattern Signal

Figure 4: This signal contains an instance of the above signal starting at time t=s1t=s1.

Longer Signal

Figure 5: This signal shows a sketch of the absolute value of the matched filter output for the interval shown. Note that this was just an "eyeball approximation" sketch. Observe the pronounced peak at time t=s1t=s1.

Absolute Value of Output