<< Chapter < Page Chapter >> Page >

To fix the instability of the Shannon representation, we assume that the signal is slightly more bandlimited than before

f ^ ( ω ) = 0 for | ω | π - δ , δ > 0 ,
and instead of using χ [ - π , π ] , we multiply by another function g ^ ( ω ) which is very similar in form to the characteristic function, but decays at its boundaries in a smootherfashion (i.e. it has more derivatives). A candidate function g ^ is sketched in .

Sketch of g ^ .

Now, it is a property of the Fourier transform that an increased smoothness in one domain translates into a faster decay in theother. Thus, we can fix our instability problem, by choosing g ^ so that g ^ is smooth and g ^ ( ω ) = 1 , | ω | π - δ and g ^ = 0 , | ω | > π . By choosing the smoothness of g suitably large, we can, for any given m 1 , choose g to satisfy

| g ( t ) | C ( | t | + 1 ) m
for some constant C > 0 .

Using such a g ^ , we can rewrite ( ) as

f ^ ( ω ) = F ( ω ) g ^ ( ω ) = 1 2 π n Z f ( n ) e - i n ω g ^ ( ω ) .
Thus, we have the new representation
f ( t ) = n Z f ( n ) g ( t - n ) ,
where we gain stability from our additional assumption that the signal is bandlimited on [ - π - δ , π - δ ] .

Does this assumption really hurt? No, not really because if our signal is really bandlimited to [ - π , π ] and not [ - π - δ , π - δ ] , we can always take a slightly larger bandwidth, say [ - λ π , λ π ] where λ is a little larger than one, and carry out the same analysis as above.Doing so, would only mean slightly oversampling the signal (small cost).

Recall that in the end we want to convert analog signals into bit streams. Thus far, we have the two representations

f ( t ) = n Z f ( n ) sinc ( π ( t - n ) ) , f ( t ) = n Z f n λ g ( λ t - n ) .
Shannon's Theorem tells us that if f B A , we should sample f at the Nyquist rate A (which is twice the support of f ^ ) and then take the binary representation of the samples. Our more stable representation saysto slightly oversample f and then convert to a binary representation. Both representations offer perfect reconstruction,although in the more stable representation, one is straddled with the additional task of choosing an appropriate λ .

In practical situations, we shall be interested in approximating f on an interval [ - T , T ] for some T > 0 and not for all time. Questions we still want to answer include

  • How many bits do we need to represent f in B A = 1 on some interval [ - T , T ] in the norm L [ - T , T ] ?
  • Using this methodology, what is the optimal way of encoding?
  • How is the optimal encoding implemented?

Towards this end, we define

B A : = { f L 2 ( R ) : | f ^ ( ω ) | = 0 , | ω | A π } .
Then for any f B A , we can write
f = n f n A · sinc π ( A t - n ) .
Fourier transform of g λ ( · ) .
In other words, samples at 0, ± 1 A , ± 2 A , are sufficient to reconstruct f . Recall also that sinc ( x ) = sin ( x ) x decays poorly (leading to numerical instability). We can overcome this problem byslight over-sampling. Say we over-sample by a factor λ > 1 . Then, we can write

f = f n λ A g λ ( λ A t - n ) .

Hence we need samples at 0, ± 1 λ A , ± 2 λ A , etc. What is the advantage? Sampling more often than necessary buys us stability because we now have a choicefor g λ ( · ) . If we choose g λ ( · ) infinitely differentiable whose Fourier transform looks as shown in we can obtain

| g λ ( t ) | c λ , k ( 1 + | t | ) k , k = 1 , 2 , . . .
and therefore g λ ( · ) decays very fast. In other words, a sample's influence is felt only locally. Note however, thatover-sampling generates basis functions that are redundant (linearly dependent), unlike the integer translates of the sinc ( · ) function.

To reconstruct signals in [ - T , T ] , the sampling interval is [ - c T , c T ] .

If we restrict our reconstruction to t in the interval [ - T , T ] , we will only need samples only from [ - c T , c T ] , for c > 1 (see ), because the distant samples will have little effect on the reconstruction in [ - T , T ] .

Get Jobilize Job Search Mobile App in your pocket Now!

Get it on Google Play Download on the App Store Now




Source:  OpenStax, Compressive sensing. OpenStax CNX. Sep 21, 2007 Download for free at http://cnx.org/content/col10458/1.1
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Compressive sensing' conversation and receive update notifications?

Ask