Abstract: A method of blind signal separation of convolutively mixed signals comprises firstly processing signals to produce second order independence. In a second step, and together with ranges of signal delay and rotation parameters, the resulting processed signals are used to determine delay and rotation parameters. These parameters implement at least one elementary paraunitary matrix and transform the processed signals into output signals with improvement in independence to at least a predominant part of a maximum extent obtainable over the parameter ranges. The output signals then become the next processed signals and the second step is iterated until independence ceases to be improved significantly. The latest output signals are then unmixed signals.

Abstract: A method for dynamic blind signal separation generates initialisation information by processing an immediately preceding data window. This information is input at (21) and used at (22) to initialize orthogonality of data in an immediately following window. Initialised data are decorrelated at (23) with small update angles using a Jacobi technique. Steps (22) and (23) are collectively a second order stage of processing in statistical terms producing orthonormal signals. The orthonormal signals are initialized at (25) and then undergo separation at (27) by ICA with small angle updates using statistics higher than second order to produce separated signals. The method may be implemented in an acquisition phase to separate signals and among them identify desired signals, and a subsequent phase in which only the desired signals are separated.

Abstract: A method of strongly decorrelating signals comprising processing input signals to determine delay and rotation parameters which implement at least one elementary paraunitary matrix. The parameters transform the input signals into output signals with improvement in a measure of strong decorrelation. The improvement in the measure of strong decorrelation is then assessed: if it is significant the latest output signals are, designated as input signals and the process is iterated. Iteration continues until the measure of strong decorrelation ceases to improve significantly, at which point the latest output signals are designated as signals decorrelated in a wide sense.

Abstract: A heuristic processor incorporates a digital arithmetic unit arranged to compute the squared norm of each member of a training data set with respect to each member of a set of centers, and to transform the squared norms in accordance with a nonlinear function to produce training &phgr; vectors. A systolic array arranged for QR decomposition and least mean squares processing forms combinations of the elements of each &phgr; vector to provide a fit to corresponding training answers. The form of combination is then employed with like-transformed to provide estimates of unknown result. The processor is applicable to provide estimated results for problems which are nonlinear and for which explicit mathematical formalisms are unknown.