Separation of signals with overlapping spectra using signal characterisation and hyperspace filtering

For separation of signals with overlapping spectra. Classical linear filters fail to perform effectively. Nonlinear filters such as Volterra filters or artificial neural networks (ANNs) can perform better but their implementations are often impractical due to their computational complexity. In this paper an ANN based hyperspace signal modeling is used to separate signals with overlapping spectra. The computational complexity of the ANN is reduced significantly by a simple feature extraction utilizing the unique temporal characteristics of the signals. The results show that difficult signal separation and filtering can be achieved efficiently by employing an ANN and an effective feature extraction