A Clustering-Based Approach for Designing Low Complexity FIR Filters

Abstract

Low power requirements, real-time constraints and big data applications present several challenges to digital signal processing (DSP) that span multiple domains including filtering and frequency analysis. Design of digital filters that can efficiently process signals with low computational complexity is desirable and requires innovative approaches. In this paper, we present an efficient two-step design methodology to design low complexity finite-impulse response (FIR) filters. In the first step, the filter design is formulated as an optimization problem to find the minimum number of coefficients, based on K-means clustering with a Euclidean distance metric. In the second step, a mapping approach is proposed to compensate for the mean square error (MSE) that results from the clustering. We compare the proposed method with three different methods from the literature to demonstrate its effectiveness in terms of computational complexity without compromising the filtering performance.