A Low-Complexity Nonlinear Least Mean Squares Filter Based on a Decomposable Volterra Model

Nonlinear signal processing is important in various applications, however it usually requires high computational cost. This paper proposes a low-complexity nonlinear adaptive least mean squares (LMS) filter based on decomposable Volterra kernels. The decomposability condition comes from a well-posed approximation problem, which imposes a rank-one structure on the full Volterra model, resulting in a system equivalent to a product of ordinary...