Midcourse Clutter Mitigation Using Multi-spectral Adaptive Signal Processing

A key midcourse discrimination problem for space-based, optical sensors is the detection of an RV in a cluttered environment, where the clutter characteristics are not well characterized. Typical clutter scenarios include a dense fuel fragment environmentas well as a chaff cloud. PRA proposes to apply adaptive signal processing techniques to operate on multi-spectral measurements to suppress the clutter. The algorithm assumes that the clutter is the dominant contributor to the focal plane intensities anduses the measurements to spectrally characterize the clutter and then removes the clutter component from the signals. The algorithm uses minimal a priori assumptions and adapts directly to the clutter statistics. Preliminary results using a prototypealgorithm indicate that the RV is consistently detected in dense fuel fragment and chaff environments with few false alarms. For this effort we are proposing to add new functionality to the algorithm, analyze which bands are best suited for the differentmissions, and provide a detailed performance assessment against a number of scenarios. One key algorithmic addition proposed here is the incorporation of intensity estimation logic, which would enable the potential for multiple objects in clutter to bediscriminated. The immediate benefit will be to make midcourse discrimination algorithms more robust by reducing their dependency on a priori information and extending their operation to highly cluttered environment. This technology is directlyapplicable to systems such as SBIRS Low as well as GBI. It offers the commercial potential of developing a programmable logic array that would be a key product in a low cost interceptor system.