LMS Algorithms for Multilinear Forms

Solving a high-dimension system identification problem could involve significant challenges in terms of complexity and accuracy of the solution. Due to the large parameter space, a decomposition-based approach fits very well in this context. This was the idea behind the recently developed iterative Wiener filter for multilinear forms, which reformulates the problem using a combination of shorter filters. Nevertheless, there are inherent limitations related to the Wiener solution, while the least-mean-square (LMS) adaptive filter would represent a more practical alternative. Consequently, in this paper, we develop LMS-based algorithms for multilinear forms, in the context of a multiple-input/single-output system identification problem. Simulation results indicate the good performance of the proposed algorithms, especially in terms of their fast convergence features.