A Nonlinear Adaptive Filter Based on the Model of Simple Multilinear Functionals
This work addresses the computational bottleneck in nonlinear adaptive filtering for signal processing applications, offering an incremental improvement over existing methods.
The paper tackles the complexity of nonlinear adaptive filtering by proposing a simple multilinear functional model, which reduces computational demands while maintaining performance. It demonstrates favorable convergence and complexity compared to existing polynomial algorithms in system identification tests.
Nonlinear adaptive filtering allows for modeling of some additional aspects of a general system and usually relies on highly complex algorithms, such as those based on the Volterra series. Through the use of the Kronecker product and some basic facts of tensor algebra, we propose a simple model of nonlinearity, one that can be interpreted as a product of the outputs of K FIR linear filters, and compute its cost function together with its gradient, which allows for some analysis of the optimization problem. We use these results it in a stochastic gradient framework, from which we derive an LMS-like algorithm and investigate the problems of multi-modality in the mean-square error surface and the choice of adequate initial conditions. Its computational complexity is calculated. The new algorithm is tested in a system identification setup and is compared with other polynomial algorithms from the literature, presenting favorable convergence and/or computational complexity.