Abstract
An approach known as state dependent embedding for developing nonlinear adaptive filters is presented. Many types of nonlinear filters including Volterra, bilinear, and polynomial autoregressive (PAR) are unified under this method. By recognizing the functional relationships between the channels of an equivalent linearly embedded system, state dependent embedding creates much more efficient filters than previous approaches. A filter called the layered structure emerges from the embedding. Its virtues include low computation, modularity, and local adaptation, allowing nonlinear filters to be implemented with linear adaptive building blocks. The layered structure is very amenable to VLSI. The state dependent embedding can also be used to develop very efficient lattice filters. >
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