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Magnitude l_p problem

Module by: Ricardo Vargas. E-mail the author

In some applications, the effects of phase are not a necessary factor to consider when designing a filter. For these applications, control of the filter's magnitude response is a priority for the designer. In order to improve the magnitude response of a filter, one must not explicitly include a phase, so that the optimization algorithm can look for the best filter that approximates a specified magnitude, without being constrained about optimizing for a phase response too.

Power approximation formulation

The magnitude approximation problem can be formulated as follows:

min h D ( ω ) - | H ( ω ; h ) | p p min h D ( ω ) - | H ( ω ; h ) | p p
(1)

Unfortunately, the second term inside the norm (namely the absolute value function) is not differentiable when its argument is zero. Although one could propose ways to work around this problem, I propose the use of a different design criterion, namely the approximation of a desired magnitude squared. The resulting problem is

min h D ( ω ) 2 - | H ( ω ; h ) | 2 p p min h D ( ω ) 2 - | H ( ω ; h ) | 2 p p
(2)

The autocorrelation r(n)r(n) of a causal length-NN FIR filter h(n)h(n) is given by

r ( n ) = h ( n ) * h ( - n ) = k = - ( N - 1 ) N - 1 h ( k ) h ( n + k ) r ( n ) = h ( n ) * h ( - n ) = k = - ( N - 1 ) N - 1 h ( k ) h ( n + k )
(3)

The Fourier transform of the autocorrelation r(n)r(n) is known as the Power Spectral Density function [2] R(ω)R(ω) (or simply the SPD), and is defined as follows,

R ( ω ) = n = - ( N - 1 ) N - 1 r ( n ) e - j ω n = n = - ( N - 1 ) N - 1 k = - ( N - 1 ) N - 1 h ( n ) h ( n + k ) e - j ω n R ( ω ) = n = - ( N - 1 ) N - 1 r ( n ) e - j ω n = n = - ( N - 1 ) N - 1 k = - ( N - 1 ) N - 1 h ( n ) h ( n + k ) e - j ω n
(4)

From the properties of the Fourier Transform [1] one can show that there exists a frequency domain relationship between h(n)h(n) and r(n)r(n) given by

R ( ω ) = H ( ω ) · H * ( - ω ) = | H ( ω ) | 2 R ( ω ) = H ( ω ) · H * ( - ω ) = | H ( ω ) | 2
(5)

This relationship suggests a way to design magnitude-squared filters, namely by using the filter's autocorrelation coefficients instead of the filter coefficients themselves. In this way, one can avoid the use of the non-differentiable magnitude response.

An important property to note at this point is the fact that since the filter coefficients are real, one can see from Equation 3 that the autocorrelation function r(n)r(n) is symmetric; thus it is sufficient to consider its last NN values. As a result, the PSD can be written as

R ( ω ) = n r ( n ) e - j ω n = r ( 0 ) + n = 1 N - 1 2 r ( n ) cos ω n R ( ω ) = n r ( n ) e - j ω n = r ( 0 ) + n = 1 N - 1 2 r ( n ) cos ω n
(6)

in a similar way to the linear phase problem.

The symmetry property introduced above allows for the use of the lplp linear phase algorithm of (Reference) to obtain the autocorrelation coefficients of h(n)h(n). However, there is an important step missing in this discussion: how to obtain the filter coefficients from its autocorrelation. To achieve this goal, one can follow a procedure known as Spectral Factorization. The objective is to use the autocorrelation coefficients rRNrRN instead of the filter coefficients hRNhRN as the optimization variables. The variable transformation is done using Equation 7, which is not a one-to-one transformation. Because of the last result, there is a necessary condition for a vector rRNrRN to be a valid autocorrelation vector of a filter. This is summarized [3] in the spectral factorization theorem, which states that rRNrRN is the autocorrelation function of a filter h(n)h(n) if and only if R(ω)0R(ω)0 for all ω[0,π]ω[0,π]. This turns out to be a necessary and sufficient condition [3] for the existence of r(n)r(n). Once the autocorrelation vector rr is found using existing robust interior-point algorithms, the filter coefficients can be calculated via spectral factorization techniques.

Assuming a valid vector rRNrRN can be found for a particular filter hh, the problem presented in Equation 1 can be rewritten as

L ( ω ) 2 R ( ω ) U ( ω ) 2 ω [ 0 , π ] L ( ω ) 2 R ( ω ) U ( ω ) 2 ω [ 0 , π ]
(7)

In Equation 7 the existence condition R(ω)0R(ω)0 is redundant since 0L(ω)20L(ω)2 and, thus, is not included in the problem definition. For each ωω, the constraints of Equation 7 constitute a pair of linear inequalities in the vector rr; therefore the constraint is convex in rr. Thus the change of variable transforms a nonconvex optimization problem in hh into a convex problem in rr.

References

  1. Proakis, John G. and Manolakis, Dimitris G. (1988). Digital Signal Processing. Macmillan Publishing Co.
  2. Shanmugan, K. Sam and Breipohl, A. M. (1988). Random Signals: Detection, Estimation and Data Analysis. John Wiley & Sons.
  3. Wu, Shao-Po and Boyd, Stephen and Vandenberghe, Lieven. FIR Filter design via Spectral Factorization and Convex Optimization. [To Appear in Applied Computational Control, Signal and Communications, Biswa Datt editor, Birkhauser, 1997.].

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