Processing techniques for Multiple Wavelength Infrared Sensors

This proposal extends the maximum likelihood concept as applied to the adaptive detection of unresolved, sub-pixel targets with unknown spectral signatures. This technique was developed by current ISI personnel. The clutter is modeled stochastically with a spatial - spectral covariance matrix. The target model is partially stochastic and partially deterministic. Within any given spectral band the spatial target signature is deterministic. For the sub-pixel target application, a system point spread function (PSF) is used. The PSF is allowed to vary spectrally, due to the dependency of a sensor's diffractive PSF on the spectral wavelength. The spectral target signature is completely stochastic and must be determined at each pixel using maximum likelihood estimation techniques. Based on these assumptions, an optimal maximum likelihood processor is derived. ISI proposes to expand upon encouraging performance results on real IR data. Detection probabilities are shown in many cases to improve significantly when compared to spatial-only detection processes.