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Optimal design and verification of temporal and spatial filters using second-order cone programming approach

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Abstract

Temporal filters and spatial filters are widely used in many areas of signal processing. A number of optimal design criteria to these problems are available in the literature. Various computational techniques are also presented to optimize these criteria chosen. There are many drawbacks in these methods. In this paper, we introduce a unified framework for optimal design of temporal and spatial filters. Most of the optimal design problems of FIR filters and beamformers are included in the framework. It is shown that all the design problems can be reformulated as convex optimization form as the second-order cone programming (SOCP) and solved efficiently via the well-established interior point methods. The main advantage of our SOCP approach as compared with earlier approaches is that it can include most of the existing methods as its special cases, which leads to more flexible designs. Furthermore, the SOCP approach can optimize multiple required performance measures, which is the drawback of earlier approaches. The SOCP approach is also developed to optimally design temporal and spatial two-dimensional filter and spatial matrix filter. Numerical results demonstrate the effectiveness of the proposed approach.

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References

  1. Van Veen, B. D., Buckley, K. M., Beamforming: A versatile approach to spatial filtering, IEEE ASSP Magazine, 1988, 5(2): 4–24.

    Article  Google Scholar 

  2. Oppenheim, A. V., Schafer, R. W., Disctete-time Signal Processing, Englewoods Cliffs, NJ: Prentice-Hall, 1989.

    Google Scholar 

  3. Rabiner, L. R., Gold, B., Theory and Application of Digital Signal Processing, Englewoods Cliffs, NJ: Prentice-Hall, 1975.

    Google Scholar 

  4. Widrow, B., Stearns, S. D., Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985.

    MATH  Google Scholar 

  5. Zhang, X., Dai, S., Designs of Chebyshev-type complex FIR filters and digital beamformers with linear-phase characteristics, IEE Proceedings-Vision, Image and Signal Processing, 1994, 141(1): 2–8.

    Article  Google Scholar 

  6. Lertniphonphun, W., McClellan, J. H., Complex frequency response FIR filter design, in Proc. ICASSP’98, Seattle, WA, USA, 1998, 3: 1301–1304.

    Google Scholar 

  7. Zhu, W. P., Ahmad, M. O., Swamy, M. N. S., A new approach for weighted least-square design of FIR filters, in Proc. ISCAS’99, Orlando, FL, USA, 1999, 3: 267–270.

    Google Scholar 

  8. Burrus, C. S., Barreto, J. A., Selesnick, I. W., Iterative reweighted least-squares design of FIR filters, IEEE Trans. Signal Processing, 1994, 42(11): 2926–2936.

    Article  Google Scholar 

  9. Lang, M., Bamberger, J., Nonlinear phase FIR filter design according to the L2 norm with constraints for the complex error, Signal Processing, 1994, 36(1): 31–40.

    Article  MATH  Google Scholar 

  10. Dam, H. H., Teo, K. L., Nordebo, S., Cantoni, A., The dual parameterization approach to optimal least square FIR filter design subject to maximum error constraints, IEEE Trans. Signal Processing, 2000, 48(8): 2314–2320.

    Article  MATH  MathSciNet  Google Scholar 

  11. Adams, J. W., FIR digital filters with least-squares stopbands subject to peak-gain constraints, IEEE Trans. Circuits and Systems, 1991, 39(4): 376–388.

    Article  Google Scholar 

  12. Er, M. H., Siew, C. K., Design of FIR filters using quadratic programming approach, IEEE Trans. Circuits and Systems II: Analog and Digital Signal Processing, 1995, 42(3): 217–220.

    Article  MATH  Google Scholar 

  13. Dolph, C. L., A current distribution for broadside arrays which optimizes the relationship between beamwidth and sidelobe level, Proc. IRE, 1946, 34(6): 335–348.

    Google Scholar 

  14. Ma, Y. L., Beampattern optimization for arbitrary geometry sensor arrays, Shipbuilding of China (in Chinese), 1984, 87(4): 78–85.

    Google Scholar 

  15. Olen, C. A., Compton, R. T. Jr., A numerical pattern synthesis algorithm for arrays, IEEE Trans. Antennas Propagat., 1990, 38(10): 1666–1676.

    Article  Google Scholar 

  16. Nordebo, S., Zang, Z., Claesson, I., A semi-infinite quadratic programming algorithm with applications to array pattern synthesis, IEEE Trans. Circuits and Systems II, 2001, 48(3): 225–232.

    Article  Google Scholar 

  17. Ng, B. P., Er, M. H., Kot, C., A flexible array synthesis method using quadratic programming, IEEE Trans. Antennas Propagat., 1993, 41(11): 1541–1550.

    Article  Google Scholar 

  18. Wu, L., Zielinski, A., Equivalent linear array approach to array pattern synthesis, IEEE J. Ocean. Eng., 1993, 18(1): 6–14.

    Article  Google Scholar 

  19. Wu, L., Zielinski, A., An iterative method for array pattern synthesis, IEEE J. Ocean. Eng., 1993, 18(3): 280–286.

    Article  Google Scholar 

  20. Zhou, P. Y., Ingram, M. A., Pattern synthesis for arbitrary arrays using an adaptive array method, IEEE Trans. Antennas Propagat., 1999, 47(5): 862–869.

    Article  Google Scholar 

  21. Zhu, W. J., Sun, J. C., Zeng, X. Y., Adaptive synthesis method for broadband array with frequency invariant beam pattern, Chinese Journal of Acoustics, 2003, 22(4): 352–359.

    Google Scholar 

  22. Capon, J., High-resolution frequency-wavenumber spectrum analysis, Proc. IEEE, 1969, 57(8): 1408–1418.

    Article  Google Scholar 

  23. Cox, H., Zeskind, R. M., Kooij, T., Practical supergain, IEEE Trans. Acoust., Speech, Signal Processing, 1986, 34(3): 393–398.

    Article  Google Scholar 

  24. Cox, H., Zeskind, R. M., Owen, M. M., Robust adaptive beamforming, IEEE Trans. Acoust., Speech, Signal Processing, 1987, 35(10): 1365–1376.

    Article  Google Scholar 

  25. Carlson, B. D., Covariance matrix estimation errors and diagnonal loading in adaptive arrays, IEEE Trans. Aerospace Electron. Syst., 1988, 24(4): 397–401.

    Article  Google Scholar 

  26. Song, H., Kuperman, W. A., Hodgkiss, W S. et al., Null broadening with snapshot-deficient covariance matrices in passive sonar, IEEE J. Oceanic Eng., 2003, 28(2): 250–261.

    Article  Google Scholar 

  27. Lobo, M., Vandenberghe, L., Boyd, S. et al., Applications of second-order cone programming, Linear Algebra Applicat., 1998, 284(1–3): 193–228.

    Article  MATH  MathSciNet  Google Scholar 

  28. Yan, S. F., Ma, Y. L., A unified framework for designing FIR filters with arbitrary magnitude and phase response, Digital Signal Processing, 2004, 14(6): 510–522.

    Article  Google Scholar 

  29. Gao, H. J., Wang, C. H., New approaches to robust l 2-l and H filtering for uncertain discrete-time systems, Science in China, Series F, 2003, 46(5): 355–370.

    Article  MATH  MathSciNet  Google Scholar 

  30. Vandenberghe, L., Boyd, S., Semidefinite programming, SIAM Review, 1996, 38(3): 49–95.

    Article  MATH  MathSciNet  Google Scholar 

  31. Sturm, J. F., Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones, Optim. Meth. Softw., 1999, 11–12(1–4): 625–653.

    MathSciNet  Google Scholar 

  32. Yan, S. F., Ma, Y. L., Sun, C., Optimal beamforming for arbitrary arrays using second-order cone programming, Chinese Journal of Acoustics, 2005, 24(1): 1–9.

    Google Scholar 

  33. Yan, S. F., Ma, Y. L., Frequency invariant beamforming via optimal array pattern synthesis and FIR filters design, Chinese Journal of Acoustics, 2005, 24(3): 202–211.

    MathSciNet  Google Scholar 

  34. Tao, H. H., Yu, J., Wang, H. Y. et al., A novel space-borne antenna anti-jamming technique based on immunity genetic algorithm-maximum likelihood, Science in China, Series F, 2005, 48(3): 397–408.

    Article  Google Scholar 

  35. Yan, S. F., Ma, Y. L., Frequency invariant beamforming via jointly optimizing spatial and frequency responses, Progress in Natural Science, 2005, 15(4): 368–374.

    MathSciNet  Google Scholar 

  36. Ma, Y. L., Liu, M. A., Zhang, Z. B. et al., Receiving response of towed line array to the noise of the tow ship in shallow water, Chinese Journal of Acoustics, 2003, 22(1): 1–10.

    MATH  Google Scholar 

  37. Baggeroer, A. B., Kuperman, W. A., Mikhalevsky, P. N., An overview of matched field methods in ocean acoustics, IEEE J. Ocean. Eng., 1993, 18(4): 401–424.

    Article  Google Scholar 

  38. Ma, Y. L., Yan, S. F., Yang, K. D., Matched field noise suppression: Principle with application to towed hydrophone line array, Chinese Science Bulletin, 2003, 48(12): 1207–1211.

    Article  Google Scholar 

  39. Poter, M. B., The KRAKEN normal mode program, SACLANTCEN Memo, SM-245, 1991.

  40. Yan, S. F., Ma, Y. L., Matched field noise suppression: A generalized spatial filtering approach, Chinese Science Bulletin, 2004, 49(20): 2220–2223.

    Article  MATH  Google Scholar 

  41. Gingras, D. F., Gerstoft, P., Inversion for geometric and geoacoustic parameters in shallow water: Experimental results, J. Acoust. Soc. Am., 1995, 97(6): 3589–3598.

    Article  Google Scholar 

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Correspondence to Yan Shefeng.

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Yan, S., Ma, Y. Optimal design and verification of temporal and spatial filters using second-order cone programming approach. SCI CHINA SER F 49, 235–253 (2006). https://doi.org/10.1007/s11432-006-0235-3

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  • DOI: https://doi.org/10.1007/s11432-006-0235-3

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