Reconfigurable Subaperturing for Endo-clutter Processing

The objective of this effort is to develop and demonstrate innovative approaches to dynamically configure array antenna sub-aperture architecture for enhanced endo-clutter processing, while operating in complex signal environments. Radar performance is driven by the illumination of the target and clutter by the antenna, the waveform spectral characteristics, number of sub-apertures employed, size, configuration, and degree-of-overlap. Several constraints will be used in the development. The output SINR is first used since the solutions are typically easier and will be used as baseline. The probabilities of detection and false alarm are then used as constraints, since they dictate radar operations. It involves using the appropriate probability density functions of the underlying clutter, which can be cumbersome in some cases. A Monte Carlo approach is used if closed form expressions are available. Knowledge-aided prediction and estimation techniques for adaptive sub-aperturing are then developed, since they complement traditional adaptive techniques by aggregating information of the clutter environment from both observations and other data sources. Finally, a joint adaptive sub-aperturing technique and parametric approach, which can further improve performance, will be developed. This approach is particularly useful for target detection in heterogeneous environments or in environments where the training data is range-dependent.