Extending the threshold of the eigenstructure methods

We present new eigenstructure based methods for spatial-temporal processing in passive sensor arrays. Unlike the existing methods of Schmidt and Bienvenu and Kopp, the new estimators are not based only on the underlying orthogonality relation between the "noise" subspace and the "signal" subspace, but also on statistical considerations stemming from the structure of the maximum likelihood estimator. As such, these estimators make better use of the available data and therefore have superior performance, especially in the threshold region, where efficient utilization of the data is most rewarding.