Data-selective uniform probability density function for adaptive filtering - Wang, Qizhen; Wang, Gang (Digital signal processing, 2025)
Data-selective (DS) adaptive filtering algorithms based on the Gaussian probability density function (PDF) can reduce the computation burden while preserving the estimation accuracy at a suitable data update rate. When the data update rate is relatively low, its performance may degrade. In this paper, a new discovery about DS strategies is presented. We demonstrate that under the uniform PDF, the DS strategy not only lessens computational burden, but also reduces the steady-state mean square deviation (SS-MSD) of adaptive filtering algorithms, even at substantially low update rates. A novel probabilistic model to describe the actual PDF faced by uniform-based DS adaptive filtering algorithms is proposed, termed the DS-uniform PDF (DS-UPDF), along with its mathematical property analysis. Furthermore, corresponding data-selective uniform least mean square (DS-ULMS) and data-selective uniform recursive least squares (DS-URLS) algorithms are developed. Utilizing properties of the DS-UPDF, respective analyses of mean value and mean square stability are also provided in detail. Additional tests are conducted using data-selective uniform least mean fourth (DS-ULMF) and data-selective uniform generalized maximum correntropy criterion (DS-UGMCC), confirming the universality of performance enhancement phenomena in DS adaptive filtering under uniform distributions. All empirical simulations align well with theoretical predictions.