Diffusion Pipelined Spline Adaptive Filter

Diffusion spline filters have been widely studied due to their low computational complexity. The current diffusion spline methods, aimed at external input flows at one moment, are categorized into single spline activation function schemes and multiple parallel spline activation function schemes. The former exhibits limited nonlinearity, whereas the latter, despite improvements, does not procure additional learning information to significantly augment its nonlinear capabilities. Hence, this paper proposes a Diffusiion Pipelined Spline Adaptive Filter (D-PNSF). The filter enhances the model's nonlinear identification capability by cascading spline modules, achieves synchronous processing of asynchronous information, and minimally increases time consumption. Experiments demonstrate that compared to recently proposed diffusion spline filters, D-PNSF can better learn potential nonlinear systems and achieve lower steady-state errors.