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A family of random and random type projections for radar STAP

In radar space time adaptive processing (STAP), training data from neighboring range cells is limited. This precludes implementation of the full-dimension adaptive detectors, i.e. the minimum variance distortionless response (MVDR) filter. In this paper we reduce the dimension of the problem by random sampling, i.e. by projecting the data into a random d-dimensional subspace. This offers two advantages, first, it permits implementation of classical detectors in the limited sample size regime. Second, it offers significant computational savings permitting possible real time solutions. Both these advantages are however at the cost of reducing the output SINR for radar STAP. In STAP, the cell under test is assumed to have known desired spatial and temporal responses. To ameliorate over this SINR loss from random projections, we propose other techniques where the lower dimension subspace is not entirely random, but is decomposed into both random and deterministic parts. The family of random and random type projections we develop here, are either l2 norm preserving or contraction mappings in statistical expectation.