No abstract

We describe a new approach for performing pseudo-imaging of point energy sources from spectral-temporal sensor data. Pseudo-imaging, which involves the automatic localization, spectrum estimation, and identification of energetic sources, can be difficult for dim sources and/or noisy images, or in data containing multiple sources which are closely spaced such that their signatures overlap. The new approach is specifically designed for these difficult cases. It is developed within the framework of modeling field theory (MFT), a biologically-inspired neural network system that has demonstrated practical value in many diverse areas. MFT performs an efficient optimization over the space of all model parameters and mappings between image pixels and sources, or clutter. The optimized set of parameters is then used for detection, localization and identification of the multiple sources in the data. The paper includes results computed from experimental spectrometer data.