Abstract
Challenges for optimal design of infinite impulse response digital filters include the high nonconvexity of design problem and inevitable stability constraints on the filters. To reduce the nonconvexity and tackle the stability constraints, a sequential partial optimization (SPO) algorithm was recently developed to divide the design problem into a sequence of subproblems, each updating only two second-order denominator factors. But the convergence of that algorithm is not guaranteed. By applying an incremental update with an optimized step length in each subproblem, this paper presents an improved SPO algorithm which is guaranteed to converge to a Karush–Kuhn–Tucker (not necessarily global) solution of the design problem. This paper also extends the SPO algorithm to a more general case where the number of denominator factors optimized in the subproblems can be any positive number smaller than half of the denominator order. Convergence performance of the algorithm is shown by the design of two example filters with typical specifications widely adopted in the literature. Comparisons with state-of-the-art methods demonstrate that the improved SPO algorithm obtains better filters than the competing methods in terms of the maximum magnitude of frequency-response error.
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References
C.K. Ahn, H. Kar, Expected power bound for two-dimensional digital filters in the Fornasini–Marchesini local state-space model. IEEE Signal Process. Lett. 22(8), 1065–1069 (2015)
C.K. Ahn, P. Shi, Hankel norm performance of digital filters associated with saturation. IEEE Trans. Circuits Syst. II Express Briefs 64(6), 720–724 (2017)
C.K. Ahn, P. Shi, Generalized dissipativity analysis of digital filters with finite-wordlength arithmetic. IEEE Trans. Circuits Syst. II Express Briefs 63(4), 386–390 (2016)
A. Antoniou, Digital Filters: Analysis, Design and Applications, 2nd edn. (McGraw-Hill, New York, 1993)
S. Dhabu, A.P. Vinod, A new time-domain approach for the design of variable FIR filters using the spectral parameter approximation technique. Circuits Syst. Signal Process. 36(5), 2154–2165 (2017)
B. Dumitrescu, R. Niemisto, Multistage IIR filter design using convex stability domains defined by positive realness. IEEE Trans. Signal Process. 52(4), 962–974 (2004)
T.-B. Deng, Design of recursive variable digital filters with theoretically guaranteed stability. Int. J. Electron. 103(12), 2013–2028 (2016)
T.-B. Deng, Stability trapezoid and stability-margin analysis for the second-order recursive digital filter. Signal Process. 118, 97–102 (2016)
C. Huang, Y.C. Lim, G. Li, A generalized lattice filter for finite wordlength implementation with reduced number of multipliers. IEEE Trans. Signal Process. 62(8), 2080–2089 (2014)
S. Jafari, P.A. Ioannou, Robust adaptive attenuation of unknown periodic disturbances in uncertain multi-input multi-output systems. Automatica 70, 32–42 (2016)
A.M. Jiang, H.K. Kwan, Minimax design of IIR digital filters using SDP relaxation technique. IEEE Trans. Circuits Syst. I Regul. Pap. 47(2), 378–390 (2010)
A.M. Jiang, H.K. Kwan, Minimax IIR digital filter design using SOCP, in Proceedings of the International Symposium on Circuits and Systems, Seattle (2008), p. 2454–2457
A.M. Jiang, H.K. Kwan, Y.P. Zhu, N. Xu, X.F. Liu, IIR digital filter design by partial second-order factorization and iterative WLS approach, in Proceedings of the International Symposium on Circuits and Systems, Montréal (2016), p. 2190–2193
X.P. Lai, Z.P. Lin, Minimax design of IIR digital filters using a sequential constrained least-squares method. IEEE Trans. Signal Process. 58(7), 3901–3906 (2010)
X.P. Lai, Z.P. Lin, H.K. Kwan, A sequential minimization procedure for minimax design of IIR filters based on second-order factor updates. IEEE Trans. Circuits Syst. II Express Briefs 58(1), 51–55 (2011)
X.P. Lai, H.L. Meng, J.W. Cao, Z.P. Lin, A sequential partial optimization algorithm for minimax design of separable-denominator 2-D IIR filters. IEEE Trans. Signal Process. 65(4), 876–887 (2017)
X.P. Lai, Optimal design of nonlinear-phase FIR filters with prescribed phase error. IEEE Trans. Signal Process. 57(9), 3399–3410 (2009)
M.C. Lang, Constrained Design of Digital Filters with Arbitrary Magnitude and Phase Responses. Ph.D. dissertation (Vienna University of Technology 1999)
M.C. Lang, Least-squares design of IIR filters with prescribed magnitude and phase responses and a pole radius constraint. IEEE Trans. Signal Process. 48(11), 3109–3121 (2000)
J.-H. Lee, S.-Y. Ku, Minimax design of recursive digital filters with a lattice denominator. IEE Proc. Vis. Image Signal Process. 143(6), 377–382 (1996)
T. Li, W.X. Zheng, New stability criterion for fixed-point state-space digital filters with generalized overflow arithmetic. IEEE Trans. Circuits Syst. II Express Briefs 59(7), 443–447 (2012)
Y.C. Lim, J.-H. Lee, C.K. Chen, R.H. Yang, A weighted least-squares algorithm for quasi-equiripple FIR and IIR filter design. IEEE Trans. Signal Process. 40(3), 551–558 (1992)
W.S. Lu, An argument-principle based stability criterion and application to the design of IIR digital filters, in Proceedings of the International Symposium on Circuits and Systems, Kos (2006), p. 4431–4434
W.S. Lu, Design of stable IIR digital filters with equiripple passband sand peak-constrained least-squares stopbands. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 46(11), 1421–1426 (1999)
W.S. Lu, T. Hinamoto, Optimal design of IIR digital filters with robust stability using conic quadratic-programming updates. IEEE Trans. Signal Process. 51(6), 1581–1592 (2003)
W.S. Lu, S.C. Pei, C.C. Tseng, A weighted least-squares method for the design of stable 1-D and 2-D IIR digital filters. IEEE Trans. Signal Process. 46(1), 1–10 (1998)
R. Niemistö, B. Dumitrescu, Simplified procedures for quasi-equiripple IIR filter design. IEEE Signal Process. Lett. 11(3), 308–311 (2004)
R.C. Nongpiur, D.J. Shpak, A. Antoniou, Improved design method for nearly linear-phase IIR filters using constrained optimization. IEEE Trans. Signal Process. 61(4), 895–906 (2013)
L.R. Rabiner, N.Y. Graham, H.D. Helms, Linear programming design of IIR digital filters with arbitrary magnitude function. IEEE Trans. Acoust. Speech Signal Process. 22(2), 117–123 (1974)
Z.-Y. Sun, C.-H. Zhang, Z. Wang, Adaptive disturbance attenuation for generalized high-order uncertain nonlinear systems. Automatica 80, 102–109 (2017)
C.C. Tseng, Design of stable IIR digital filter based on least p-power error criterion. IEEE Trans. Circuits Syst. I Regul. Pap. 51(9), 1879–1888 (2004)
C.C. Tseng, S.L. Lee, Minimax design of stable IIR digital filter with prescribed magnitude and phase responses. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 49(4), 547–551 (2002)
P.P. Vaidyanathan, Multirate Systems and Filter Banks (Prentice-Hall, Englewood Cliffs, 1993)
S.Y. Zhao, F. Liu, An adaptive risk-sensitive filtering method for Markov jump linear systems with uncertain parameters. J. Frankl. Inst. 349(6), 2047–2064 (2012)
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This work was supported by the National Nature Science Foundation of China under Grants 61573123, 61503104, U1509205.
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Appendix A. Proof of Theorem 2
Appendix A. Proof of Theorem 2
Proof
Firstly, from Lemma 1, the unique solution to problem (12) is a solution to problem (11) when the regularization parameter \({\lambda }>0\) is sufficiently small. Then, the solution \({\delta }{{\varvec{x}}}_\ell (\ell )={[\delta }{\tilde{{{\varvec{a}}}}}_{q(\ell )}^{\mathrm{T}} (\ell ), \delta {{\varvec{b}}}^{\mathrm {T}}(\ell )]^{\mathrm {T}}\) of problem (12) should satisfy the KKT conditions of problem (11). We now rewrite problem (11) as
and
Note that
where the bar over a complex-valued quantity represents the complex conjugate of that quantity. Then, by the KKT conditions of problem (A1), there must exist nonnegative Lagrangian multipliers \({\mu }(\ell ,{\omega })\ge 0\) for \({\omega }\in \varOmega \) and \({\nu }_{q(\ell ),n,m}(\ell )\ge 0\), for \(n=1,2,\ldots , n(\ell )\) and \(m=1,2, \ldots ,7\) such that
Note that \(q(\ell )\) goes from 1 to \(Q+\mathrm{sign}(J)\) repeatedly as the iteration index \(\ell \) proceeds. Then after using (7c) and (7d) and taking the limits of (A2a)–(A2g) as \(\ell \rightarrow \infty \), there must exist \({\mu }^{*}({\omega })\ge 0\) for all \({\omega }\in \varOmega \) and \(\nu _{nm}^*\ge 0\) for \(n=1,2,\ldots ,N/2\) and \(m=1,2,\ldots ,7\) such that
Equations (A3a)–(A3c) are the KKT optimality conditions for the following problem
which is equivalent to problem (5) with the same stability domain \({\tilde{S}}_q \) as defined by (4). It follows that \([({{\varvec{a}}}^{*})^{\mathrm{T}}, ({{\varvec{b}}}^{*})^{\mathrm{T}}]^{\mathrm{T}}\) is a KKT point of problem (5) with \({\tilde{S}}_q \) defined by (4). \(\square \)
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Meng, H., Lai, X., Cao, J. et al. A Sequential Partial Optimization Algorithm with Guaranteed Convergence for Minimax Design of IIR Digital Filters. Circuits Syst Signal Process 37, 4336–4362 (2018). https://doi.org/10.1007/s00034-018-0763-2
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DOI: https://doi.org/10.1007/s00034-018-0763-2