In mathematical systems theory, a multidimensional system or m-D system is a system in which not only one independent variable exists (like time), but there are several independent variables.
Important problems such as factorization and stability of m-D systems (m > 1) have recently attracted the interest of many researchers and practitioners. The reason is that the factorization and stability is not a straightforward extension of the factorization and stability of 1-D systems because, for example, the fundamental theorem of algebra does not exist in the ring of m-D (m > 1) polynomials.
A state-space model is a representation of a system in which the effect of all "prior" input values is contained by a state vector. In the case of an m-d system, each dimension has a state vector that contains the effect of prior inputs relative to that dimension. The collection of all such dimensional state vectors at a point constitutes the total state vector at the point.
Consider a uniform discrete space linear two-dimensional (2d) system that is space invariant and causal. It can be represented in matrix-vector form as follows:
Represent the input vector at each point by , the output vector by the horizontal state vector by and the vertical state vector by . Then the operation at each point is defined by:
where and are matrices of appropriate dimensions.
These equations can be written more compactly by combining the matrices:
Given input vectors at each point and initial state values, the value of each output vector can be computed by recursively performing the operation above.
Often an image processing or other md computational task is described by a transfer function that has certain filtering properties, but it is desired to convert it to state-space form for more direct computation. Such conversion is referred to as realization of the transfer function.
Consider a 2d linear spatially invariant causal system having an input-output relationship described by:
Two cases are individually considered 1) the bottom summation is simply the constant 1 2)the top summation is simply a constant . Case 1 is often called the “all-zero” or “finite impulse response” case, whereas case 2 is called the “all-pole” or “infinite impulse response” case. The general situation can be implemented as a cascade of the two individual cases. The solution for case 1 is considerably simpler than case 2 and is shown below.
Example: all zero or finite impulse response
The state-space vectors will have the following dimensions:
Each term in the summation involves a negative (or zero) power of and of which correspond to a delay (or shift) along the respective dimension of the input . This delay can be effected by placing ’s along the super diagonal in the . and matrices and the multiplying coefficients in the proper positions in the . The value is placed in the upper position of the matrix, which will multiply the input and add it to the first component of the vector. Also, a value of is placed in the matrix which will multiply the input and add it to the output .
The matrices then appear as follows: