Forward-backward averaging in the presence of array manifold errors

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 f/b averaging and, for some STAP cases, f/b 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