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In this paper, we investigate the use of forward-backward (f/b) averaging for estimating the covariance matrix used for adaptive beamforming and space-time adaptive processing (STAP). We demonstrate that the estimation loss is reduced by the use of fib averaging and, for some STAP cases, fib averaging can even quadruple the available sample support. We also show that unknown array manifold errors have little effect on the effectiveness of f/b averaging. The gain from f/b averaging is demonstrated on data from the mountaintop database. Index Terms - Adaptive arrays.

A proposed fast quasi-Newton (FQN) algorithm presented as a solution to the stochastic adaptive filtering question has a structure for efficient computational methods. However, the FQN program discards the recursive least squares (RLS) function optimality to attain decreased numerical complexity. Although FQN performance is lessened as compared to RLS, tracking performance remain the same. The FQN program does not experience finite precision errors due to recursive internal variables since FQN variables are simple, making the program easy to customize and monitor for error potentials.