Bandpass Filters: A Penalized Least-Squares Optimization With $\boldsymbol \ell _{1}$-Norm Regularization Design

This letter presents a robust framework based on penalized least-squares optimization (PLSO) with l1-norm regularization, specifically designed for the development and implementation of bandpass filters (BPFs). By integrating the sparsity-inducing properties of l1-norm regularization with the frequency selectivity inherent in conventional BPFs, this approach yields an adaptive filter capable of dynamically adjusting its parameters according to the characteristics of the input signal. This adaptability enables the filter to accurately capture variations in frequency bands while preserving edges and boundaries between them.