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.
- Pub Date:
- August 2004
- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Computer Vision and Pattern Recognition;
- 16 pages, 12 figures. Full version of a short paper that was published in the Digest of the 5th IMA International Conference on Mathematics in Signal Processing, 18-20 December 2000, Warwick University, UK