A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling

Nonlinear models are known to provide excellent performance in real-world applications that often operate in non-ideal conditions. However, such applications often require online processing to be performed with limited computational resources. In this paper, we propose a new efficient nonlinear model for online applications. The proposed algorithm is based on the linear-in-the-parameters (LIP) nonlinear filters and their implementation as functional link adaptive filters (FLAFs). We focus here on a new effective and efficient approach for FLAFs based on frequency-domain adaptive filters. We introduce the class of frequency-domain functional link adaptive filters (FD-FLAFs) and propose a partitioned block approach for their implementation. We also investigate on the functional link expansions that provide the most significant benefits operating with limited resources in the frequency-domain. We present and compare FD-FLAFs with different expansions to identify the LIP nonlinear filters showing the best tradeoff between performance and computational complexity. Experimental results prove that the frequency domain LIP nonlinear filters can be considered as an efficient and effective solution for online applications, like the nonlinear acoustic echo cancellation.