A Modular NMF Matching Algorithm for Radiation Spectra

In real-world object identification systems, the operational mission may change from day to day. For example, a target recognition system may be searching for heavy armor one day, and surface-to-air assets the next, or a radiation detection system may be interested in detecting medical isotopes in one instance, and special nuclear material in another. To accommodate this "mission of the day" type scenario, the underlying object database must be flexible and able to adjust to changing target sets. Traditional dimensionality reduction algorithms rely on a single basis set that is derived from the complete set of objects of interest, making missionspecific adjustment a significant task. In this work, we describe a method that uses many limited-size individual basis sets to represent objects of interest instead of a single unifying basis set. Thus, only the objects of interest for the mission at hand are used at any given time, and additional objects can be added to the system simply by training a basis for the new object. We demonstrate the modular identification system on the problem of identifying radioisotopes from their gamma ray spectra using nonnegative matrix factorization.