2009Unpublished venueRequires access

Design of FIR filter using constrained L1 minimization method

Chien‐Cheng Tseng, Su‐Ling Lee

Open publisher page 4 citations

Abstract

In this paper, the design of digital FIR filter is investigated. First, filter design problem is described by considering various requirements except frequency response error. Then, the constrained L1minimization method in compressed sensing is applied to design FIR filter. As a result, dynamic range of filter coefficients, upper bound of filtering output, sparsity of filter coefficients, and error of frequency response can be controlled by suitably choosing the prescribed design parameter. Finally, some numerical comparisons with conventional least-squares design method are made to demonstrate the flexibility of this new design approach.

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What this paper is about

In this paper, the design of digital FIR filter is investigated. First, filter design problem is described by considering various requirements except frequency response error. Then, the constrained L1minimization method in compressed sensing is applied to design FIR filter. As a result, dynamic range of filter coefficients, upper bound of filtering output, sparsity of filter coefficients, and error of frequency response can be controlled by suitably choosing the prescribed design parameter. Finally, some numerical comparisons with conventional least-squares design method are made to demonstrate the flexibility of this new design approach.

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Available abstract

In this paper, the design of digital FIR filter is investigated. First, filter design problem is described by considering various requirements except frequency response error. Then, the constrained L1minimization method in compressed sensing is applied to design FIR filter. As a result, dynamic range of filter coefficients, upper bound of filtering output, sparsity of filter coefficients, and error of frequency response can be controlled by suitably choosing the prescribed design parameter. Finally, some numerical comparisons with conventional least-squares design method are made to demonstrate the flexibility of this new design approach.

Key concepts: Minification, Filter (signal processing), Finite impulse response, Filter design, Computer science, Algorithm, Flexibility (engineering), Digital filter

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