2021/10/14 by J. Bonetti, Bonetti, Juan I., James Kunst +5
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #Control Systems and Identification #FOS: Electrical engineering #Signal Processing (eess.SP) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.07776
openalex publication_date 2021/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a novel system identification technique, based on a least-mean square algorithm, allowing for the estimation of a linear channel by using an unknown-response measurement channel. The key of the technique is a memoryless nonlinear function working as uncoupling block between the estimated and observation channels, conforming a Wiener-Hammerstein scheme. We prove that this estimation, only differing from the actual channel response by a scaling factor and a temporal shift, does not depend on the observation channel bandwidth. As a consequence, this technique enables the usage of low-cost measurement devices as feedback channel. We present numerical examples of the method, supporting the proposal and displaying excellent results.