Can somebody explain SDF and MACE to me?
Your question is allusive at that point: explanation of acronyms (SDF, MACE), need for composite filters... A starting point is  matched spatial filter (MSF). To overcome its limitations, several versions have been proposed. David Casasent is a common denominatoir of the ones you consider.

For the SDF filer, my reference is:


C. F. Hester and David Casasent, “Multivariant technique for multiclass pattern recognition,” Applied Optics, 1980



  A technique for multiclass optical pattern recognition of different
  perspective views of an object is described. Each multiclass
  representation of an object is described as an orthonormal basis
  function expansion, and a single averaged matched spatial filter is
  then produced from a weighted linear combination of these functions.
  The technique is demonstrated for a terminal missile guidance
  application using IR tank imagery.


with comment in:


J. B. Nelson, "Multivariant technique for multiclass pattern recognition: comment"



  In a recent article, Hester and Casasent discussed an interesting
  technique in multiclass pattern recognition. They mentioned that other
  approaches to the construction of an orthonormal basis function set
  were possible and specifically suggested the possibility of using a
  Karhunen-Loéve expansion as an alternative to their own Gram-Schmidt
  procedure. It is the purpose of this Letter to recommend yet another
  alternative, the singular value decomposition (SVD), which has very
  convenient conceptual and numerical advantages, and to show how it may
  serve as a foundation for future extensions of the theory. The scope
  of the present discussion is restricted solely to the digital
  processing aspects of Ref. 1. It will begin with a brief review of the
  problem in the slightly modified matrix notation that is more
  compatible with the SVD technique that follows.



Abhijit Mahalanobis, B. V. K. Vijaya Kumar, and David Casasent, Minimum average correlation energy filters, Applied Optics, 1987



  The synthesis of a new category of spatial filters that produces sharp
  output correlation peaks with controlled peak values is considered.
  The sharp nature of the correlation peak is the major feature
  emphasized, since it facilitates target detection. Since these filters
  minimize the average correlation plane energy as the first step in
  filter synthesis, we refer to them as minimum average correlation
  energy filters. Experimental laboratory results from optical
  implementation of the filters are also presented and discussed