Filter design by convex iteration

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LaTeX: 
</pre>
<p>H(\omega) = h(0) + h(1)e^{-j\omega} + \cdots + h(n-1)e^{-j(N-1)\omega}
</p>
<pre>

where LaTeX:  h \in \texttt{C}^\texttt{N} \vspace{5 mm}

For low pass filter, the frequency domain specifications are: \begin{equation} \begin{array}{ll} \frac{1}{\delta_1}\leq|H(\omega)|\leq\delta_1, & \omega\in[0,\omega_p]\\ |H(\omega)|\leq\delta_2, & \omega\in[\omega_s,\pi] \end{array} \end{equation}

\vspace{5 mm}

To minimize the maximum magnitude of LaTeX: h , the problem becomes \begin{equation} \begin{array}{lll} \hbox{min} LaTeX:  |h|_\infty  \\ \hbox{subject to} & \frac{1}{\delta_1}\leq|H(\omega)|\leq\delta_1, & \omega\in[0,\omega_p]\\ & |H(\omega)|\leq\delta_2, & \omega\in[\omega_s,\pi] \end{array} \end{equation}

\vspace{5 mm}

A new vector LaTeX: g \in \texttt{C}^\texttt{N*N}$ is defined as concatenation of time-shifted versions of $h , \emph{i.e.} \begin{equation} g = \left[

              \begin{array}{c}
                h(t) \\
                h(t-1) \\
                \vdots \\
                h(t-N) \\
              \end{array}
            \right]

\end{equation} Then LaTeX: gg^\texttt{H} is a positive semidefinite matrix of size LaTeX: \texttt{N}^2 \times \texttt{N}^2 with rank 1. Summing along each 2N-1 subdiagonals gives entries of the autocorrelation function of LaTeX: h . In particular, the main diagonal holds squared entries of LaTeX: h . Minimizing LaTeX: |h|_\infty is equivalent to minimizing the trace of LaTeX: gg^\texttt{H} .

\vspace{5 mm}

Using spectral factorization, an equivalent problem is \begin{equation} \begin{array}{lll} \hbox{min} & |r|_\infty & \\ \hbox{subject to} & \frac{1}{\delta_1^2}\leq R(\omega)\leq\delta_1^2, & \omega\in[0,\omega_p]\\ & R(\omega)\leq\delta_2^2, & \omega\in[\omega_s,\pi]\\ & R(\omega)\geq0, & \omega\in[0,\pi] \end{array} \end{equation}

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