Optimal Filter Coefficients: Correlation versus Least Squares

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SUMMARY

The discussion centers on the equivalence of maximizing the correlation coefficient over filter coefficients and minimizing the squared error between two functions, as presented in the paper "A Waveform Correlation Method for Identifying Quarry Explosions" by D.B. Harris. The mathematical formulation shows that maximizing the correlation coefficient, defined as ρ(a), aligns with minimizing the integral of the squared difference between the functions u(t) and v(t). The author attempted to prove this relationship using the convolution theorem but did not achieve the desired results, instead finding a connection through the polarization identity.

PREREQUISITES
  • Understanding of correlation coefficients in signal processing
  • Familiarity with integral calculus and inner product spaces
  • Knowledge of convolution theorem in the context of Fourier transforms
  • Basic concepts of optimization in mathematical functions
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  • Study the convolution theorem in detail to understand its applications in signal processing
  • Explore the polarization identity and its implications in vector spaces
  • Research optimization techniques for minimizing error functions in signal analysis
  • Examine case studies or papers that apply correlation methods in seismic data analysis
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Researchers in signal processing, mathematicians focusing on optimization techniques, and professionals analyzing seismic data or similar applications will benefit from this discussion.

Squatchmichae
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I found a claim in a paper (BSSA, Vol 81, No. 6: "A Waveform Correlation Method for Identifying Quarry Explosions", By D.B. Harris) concerning finding filter coefficients. The statement is given without proof. I cannot locate a reference or theorem for the following, and have not been able thus far to justify this claim quantitatively.

Suppose:

\begin{equation}
\mathbf{v}(t) = \displaystyle \sum_{k=1}^{N} \int_{-T}^{T} \! a_{k}(t-s) \mathbf{u}_{k}(s) \, ds,
\end{equation}

Then maximizing the correlation coefficient over filter coefficients a:

\begin{equation}
\rho(a) = \max_{a} \frac{\left\langle {\mathbf{u}(t), \mathbf{v}(t) } \right\rangle} { \sqrt{\left\langle {\mathbf{u}(t), \mathbf{u}(t) } \right\rangle \, \left\langle {\mathbf{v}(t), \mathbf{v}(t) } \right\rangle}}
\end{equation}

Is equivalent to:

\begin{equation}
\min_{a} \int_{-T}^{T} \! \parallel \mathbf{u}(t) - \mathbf{v}(t) \parallel^{2} \, dt,
\end{equation}

Where the inner product is defined by:

\begin{equation}
\left\langle {\mathbf{u}(t), \mathbf{v}(t) } \right\rangle = \int_{-T}^{T} \! {\mathbf{u}(t)}^{T} \mathbf{v}(t) \, dt,
\end{equation}

Qualitatively, this makes sense, of course. I initially attempted to prove this in the frequency domain by making use of the convolution theorem, to reduce the problem into one that looks similar to a Rayleigh quotient. This effort did not yield the correct equations.
 
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I can only see a connection via the polarization identity: $$4 \langle \mathbf{u},\mathbf{v} \rangle = ||\mathbf{u}+\mathbf{v}||^2- ||\mathbf{u}-\mathbf{v}||^2 = 4 \cdot ||\mathbf{u}||\cdot ||\mathbf{v}||\cos \sphericalangle ( \mathbf{u},\mathbf{v})$$ which says that ##\rho(a)## is maximal if the angle is close to ##90°## or if ##||\mathbf{u}+\mathbf{v}||^2## is maximal and ##||\mathbf{u}-\mathbf{v}||^2## minimal, and vice versa. Both conditions depend also on the length of the sum.
 

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