Abstract
Filter model reduction is an important optimization method in digital signal processing. A method of FIR to FIR model reduction using SDP optimization is proposed in this paper. At first, we use SDP to design an original FIR filter. Then we name a general K-order FIR digital filter H1z−1 with coefficient values equal to the first K + 1 filter coefficient values of H0z−1. Finally, we design a new general K-order FIR digital filter H2z−1 connected in parallel with H1z−1 using SDP optimization. The experiment results show this method has good performance on the magnitude error and the linear phase in passband. Therefore, this method can be used in the field of digital signal processing.
Highlights
Filter model reduction is an important optimization method in digital signal processing
When the order is higher, the design of FIR filter is more complex while the implementation of FIR filter is more difficult. erefore, when the performance requirement is not very rigorous, we use the method of FIR model reduction to design an FIR filter with the lower order instead of another FIR filter with the higher order
We present a method of FIR to FIR model reduction using semidefinite programming (SDP) optimization. e experiment results show this method has good performance on the magnitude error and the linear phase in passband
Summary
Filter model reduction is an important optimization method in digital signal processing. Erefore, when the performance requirement is not very rigorous, we use the method of FIR model reduction to design an FIR filter with the lower order instead of another FIR filter with the higher order. Reference [3] propose a comparative study based on combined special projections and important frequency-weighted model order reduction algorithms to compute optimal approximants for full-order digital filter. In Reference [4], two different algorithms for approximating FIR by IIR filters are treated: truncation of the balanced model and the Hankel-norm optimal approximation. Cain et al [5] extends the use of the balanced model truncation and Hankel-norm optimal approximation to permit close approximation of complex FIR prototypes by IIR filters. Deng et al [11] studies the design of linear phase IIR filters via optimal Hankel-norm approximation. Erefore, this method is easy to apply in the model reduction filter design
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
