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The ability to recognize a target in an image is an important problem for machine vision, surveillance systems, and
military weapons. There are many "solutions" to an automatic target recognition (ATR) problem proposed by
practitioners. Often the definition of the problem leads to multiple solutions due to the incompleteness of the definition.
Solutions are also made approximate due to resource limitations. Issues concerning "best" solution and solution
performance are very open issues, since problem definitions and solutions are ill-defined. Indeed from information
based physical measurement theory such as found in the Minimum Description Length (MDL) the exact solution is
intractable1. Generating some clarity in defining problems on restricted sets seems an appropriate approach for
improving this vagueness in ATR definitions and solutions. Given that a one to one relationship between a physical
system and the MDL exists, then this uniqueness allows that a solution can be defined by its description and a norm
assigned to that description. Moreover, the solution can be characterized by a set of metrics that are based on the
algorithmic information of the physical measurements. The MDL, however, is not a constructive theory, but solutions
can be defined by concise problem descriptions. This limits the scope of the problem and we will take this approach
here. The paper will start with a definition of an ATR problem followed by our proposal of a descriptive solution using a
union of subspaces model of images as described below based on Lu and Do2. This solution uses the concept of
informative representations3 implicitly which we review briefly. Then we will present some metrics to be used to
characterize the solution(s) which we will demonstrate by a simple example. In the discussions following the example
we will suggest how this fits in the context of present and future work.
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