  The theory of stochastic vector quantisers (SVQ) has been extended to allow
the quantiser to develop invariances, so that only "large" degrees of freedom
in the input vector are represented in the code. This has been applied to the
problem of encoding data vectors which are a superposition of a "large" jammer
and a "small" signal, so that only the jammer is represented in the code. This
allows the jammer to be subtracted from the total input vector (i.e. the jammer
is nulled), leaving a residual that contains only the underlying signal. The
main advantage of this approach to jammer nulling is that little prior
knowledge of the jammer is assumed, because these properties are automatically
discovered by the SVQ as it is trained on examples of input vectors.

Neural and Evolutionary Computing;Computer Vision and Pattern Recognition