Coprimality of Certain Families of Integer Matrices

Commuting coprime pairs of integer matrices have been of interest in multidimensional multirate systems, and more recently in array processing. In multirate systems they arise, for example, in the design of interchangeable cascades of decimator and expander matrices. In array processing they arise in the construction of dense coarrays from sparse sensors located on a pair of lattices. For the important case of two dimensional signals, these matrices have size 2 × 2. In this paper the condition for coprimality is derived for several classes of 2 × 2 integer matrices, namely circulant, skew-circulant, and triangular families. The first two are also commuting families. For each class, the special case of adjugate pairs, which automatically commute, is also elaborated. It is also shown that the problem of testing coprimality of two 2 × 2 matrices is equvialent to testing coprimality of a pair of triangular matrices, which can be done almost by inspection. Also considered is the case of 3 × 3 triangular matrices and their adjugates, which have potential applications in three dimensional signal processing.

[1]  Peter Lancaster,et al.  The theory of matrices , 1969 .

[2]  P. P. Vaidyanathan,et al.  The role of integer matrices in multidimensional multirate systems , 1993, IEEE Trans. Signal Process..

[3]  R.N. Bracewell,et al.  Signal analysis , 1978, Proceedings of the IEEE.

[4]  P. P. Vaidyanathan,et al.  Sparse coprime sensing with multidimensional lattice arrays , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[5]  A. Kalker Commutativity of up/down sampling , 1992 .

[6]  R. Tennant Algebra , 1941, Nature.

[7]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[8]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[9]  P. Vaidyanathan,et al.  Coprime sampling and the music algorithm , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[10]  R. T. Hoctor,et al.  The unifying role of the coarray in aperture synthesis for coherent and incoherent imaging , 1990, Proc. IEEE.

[11]  Robert Bregovic,et al.  Multirate Systems and Filter Banks , 2002 .

[12]  P. P. Vaidyanathan,et al.  Theory of Sparse Coprime Sensing in Multiple Dimensions , 2011, IEEE Transactions on Signal Processing.

[13]  A. Meyer,et al.  Introduction to Number Theory , 2005 .

[14]  Jelena Kovacevic,et al.  The commutativity of up/downsampling in two dimensions , 1991, IEEE Trans. Inf. Theory.

[15]  James H. McClellan,et al.  Rules for multidimensional multirate structures , 1994, IEEE Trans. Signal Process..

[16]  L. Mirsky,et al.  The Theory of Matrices , 1961, The Mathematical Gazette.

[17]  Anamitra Makur,et al.  Enumeration of Downsampling Lattices in Two-Dimensional Multirate Systems , 2008, IEEE Transactions on Signal Processing.

[18]  F. R. Gantmakher The Theory of Matrices , 1984 .

[19]  P. P. Vaidyanathan,et al.  Sparse Sensing With Co-Prime Samplers and Arrays , 2011, IEEE Transactions on Signal Processing.

[20]  Dan E. Dudgeon,et al.  Multidimensional Digital Signal Processing , 1983 .

[21]  B. Evans,et al.  Designing commutative cascades of multidimensional upsamplers and downsamplers , 1997, IEEE Signal Processing Letters.