Optimizing Subband Adaptive Filters for Resilience Against Unanticipated Signal Truncation

Abstract

This letter addresses a common issue in engineering applications: unanticipated signal truncation events caused by the mismatch between the operational range of measurement devices and the signals to be measured. Under such circumstances, the conventional normalized subband adaptive filtering (NSAF) algorithm significantly underperforms and may even fail to converge. To tackle this issue, we propose an improved NSAF algorithm. We introduce an expectation maximization framework to address the maximum likelihood estimation before the subband adaptive filter, specifically to handle double-sided signal truncation. This new approach leads to an NSAF for unanticipated truncation (UT-NSAF), which has been theoretically and numerically proven to be unbiased. Importantly, our research demonstrates that UT-NSAF significantly outperforms other algorithms in terms of estimation accuracy and convergence speed. Notably, the steady-state solution of UT-NSAF remains almost unaffected by varying truncation thresholds, showing robustness crucial for dealing with various unexpected signal truncation scenarios in engineering applications.