The helical coordinate |

Let us have an example.
Consider a filter like the familiar time derivative
except
let us downweight the
a tiny bit, say
where
.
Now the filter
has a spectrum
with autocorrelation
coefficients
that look a lot like a second
derivative, but it is a tiny bit bigger in the middle.
Two different waveforms,
and its time reverse
both have the same autocorrelation.
Spectral factorization could give us both
and
but we always want the one that is CwCI.
The bad one is weaker on its first pulse.
Its inverse is not causal.
Below are two expressions for the filter inverse to
,
the first divergent
(filter coefficients at infinite lag are infinitely strong),
the second convergent but noncausal.

(14) | |||

(15) |

(Please multiply each equation by and see it reduce to ).

We begin with a power spectrum and our goal is to find a CwCI filter with that spectrum. If we input to the filter an infinite sequence of random numbers (white noise) we should output something with the original power spectrum.

We easily inverse Fourier transform the square root of the power spectrum getting a symmetrical time function, but we need a function that vanishes before . On the other hand, if we already had a causal filter with the correct spectrum we could manufacture many others. To do so all we need is a family of delay operators to convolve with. A pure delay filter does not change the spectrum of anything. Same for frequency-dependent delay operators. Here is an example of a frequency-dependent delay operator: First convolve with (1,2) and then deconvolve with (2,1). Both these have the same amplitude spectrum so their ratio has a unit amplitude (and nontrivial phase). If you multiply by its Fourier conjugate (replace by ) the resulting spectrum is 1 for all .

Anything whose nature is delay is death to CwCI. The CwCI has its energy as close as possible to . More formally, my first book, FGDP, proves that the CwCI filter has for all time more energy between and than any other filter with the same spectrum.

Spectra can be factorized by an amazingly wide variety of techniques, each of which gives you a different insight into this strange beast. They can be factorized by factoring polynomials, by inserting power series into other power series, by solving least squares problems, by taking logarithms and exponentials in the Fourier domain. I've coded most of them and still find them all somewhat mysterious.

Theorems in Fourier analysis can be interpreted physically in two different ways, one as given, the other with time and frequency reversed. For example, convolution in one domain amounts to multiplication in the other. If we were to express the CwCI concept with reversed domains, instead of saying the ``energy comes as quick as possible after '' we would say ``the frequency function is as close to as possible.'' In other words, it is minimally wiggly with time. Most applications of spectral factorization begin with a spectrum, a real, positive function of frequency. I once achieved minor fame by starting with a real, positive function of space, a total magnetic field measured along the -axis and I reconstructed the magnetic field components and that were minimally wiggly in space (FGDP p.61).

The helical coordinate |

2013-07-25