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Adaptive Lattice Filters for Systems of Space-Time Processing of Non-Stationary Gaussian Processes

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Abstract

Adaptive systems protecting pulse radars from non-stationary in time (range) clutter echoes are usually tuned using training vectors composed of complex amplitudes of input signals and comprising a finite-length “sliding window” of data. From any current range gate to a subsequent one, a training sample is partially updated (or modified) by means of excluding the “old” training vectors (correspond to the current range gate) and including the “new” ones (correspond to the next range gate). As a consequence, respective estimates of adaptive system parameters are corrected according to a modified sample correlation matrix (CM), which is typically a sum of an initialCMand a modifying matrix of rank K ≥ 1. In this case it is possible to avoid re-computing these parameters based on a new training sample of full size and, instead of this, we correct them in an “economical” way employing K-rank modification of a matrix inverse to the CM estimate.

This paper is devoted to comparative analysis of various (K ≥ 1)-rank modification algorithms that correct the parameters of adaptive lattice filters (ALF). Main attention is paid to synthesis as well as theoretical and experimental study of algorithms of direct (K > 1)-rank modification of the ALF parameters. These algorithms attain the said objective omitting the K-fold application of known rank-one (K = 1) modification algorithms. We also synthesize a combined algorithm (CA) of (K ≥ 1)-rank modification of the ALF parameters that is more computationally simple and more numerically robust compared to known algorithms. The ALF employing the CA can serve as an effective tool for solving various tasks of space-time adaptive signal processing in pulse radars of different purpose.

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References

  1. Y. D. Shirman (ed.), Radioelectronic Systems: Fundamentals of Construction and Theory, Handbook, 2nd ed. [in Russian] (Radiotekhnika, Moscow, 2007).

    Google Scholar 

  2. J. R. Rice, Matrix Computations and Mathematical Software (McGraw-Hill, New York, 1981).

    MATH  Google Scholar 

  3. V. V. Voevodin, E. E. Tyrtyshnikov, Computational Processes with Toeplitz Matrices [in Russian] (Nauka, Moscow, 1987).

    MATH  Google Scholar 

  4. P. E. Gill, W. Murray (eds.), Numerical Methods for Constrained Optimization (Academic Press, New York, 1974).

    Google Scholar 

  5. V. V. Voevodin, Computational Fundamentals of Linear Algebra [in Russian] (Nauka, Moscow, 1977).

    Google Scholar 

  6. H. Lev-Ari, T. Kailath, “Schur and Levinson algorithms for nonstationary processes,” in: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP’81, 30 Mar.-1 Apr. 1981, Atlanta, GA, USA (IEEE, 1981). DOI: 10.1109/ICASSP.1981.1171194.

    Google Scholar 

  7. D. I. Lekhovytskiy, “Generalized Levinson algorithm and universal lattice filters,” Radiophys. Quantum Electron. 35, No. 9–10, 509 (1992). DOI: 10.1007/BF01044971.

    Article  MathSciNet  Google Scholar 

  8. J. P. Burg, “A new analysis technique for time series data,” Proc. of NATO Advanced Study Institute on Signal Processing with Emphasis on Underwater Acoustics, Enschede, The Netherlands (Enschede, Netherlands, 1968).

    Google Scholar 

  9. F. Itakura, S. Saito, “Digital filtering techniques for speech analysis and synthesis,” Proc. of 7th Int. Congress on Acoustics, Budapest, Hungary (Akadémiai Kiadó, Budapest, 1971), Vol. 3, pp. 261–264.

    Google Scholar 

  10. A. Gray, J. Markel, “Digital lattice and ladder filter synthesis,” IEEE Trans. Audio Electroacoust. 21, No. 6, 491 (1973). DOI: 10.1109/TAU.1973.1162522.

    Article  Google Scholar 

  11. B. Friedlander, “Lattice filters for adaptive processing,” Proc. IEEE 70, No. 8, 829 (1982). DOI: 10.1109/PROC. 1982.12407.

    Article  Google Scholar 

  12. B. Friedlander, “Lattice methods for spectral estimation,” Proc. IEEE 70, No. 9, 990 (1982). DOI: 10.1109/PROC.1982.12429.

    Article  Google Scholar 

  13. C. F. N. Cowan, P. M. Grant (eds.), Adaptive Filters (Prentice Hall, Englewood Cliffs, NJ, 1985).

    MATH  Google Scholar 

  14. A. H. Sayed, Fundamentals of Adaptive Filtering (John Wiley & Sons, Hoboken, NJ, 2003).

    Google Scholar 

  15. V. I. Djigan, Adaptive Filtering of Signals: Theory and Algorithms [in Russian] (Tekhnosfera, Moscow, 2013).

    Google Scholar 

  16. D. I. Lekhovytskiy, D. S. Rachkov, A. V. Semeniaka, V. P. Riabukha, D. V. Atamanskiy, “Adaptive lattice filters. Part I. Theory of lattice structures,” Prikladnaya Radioelektronika 10, No. 4, 380 (2011).

    Google Scholar 

  17. D. I. Lekhovytskiy, D. S. Rachkov, A. V. Semeniaka, V. P. Riabukha, D. V. Atamanskiy, “Adaptive lattice filters. Part II. Algorithms for ALF adjustment,” Prikladnaya Radioelektronika 10, No. 4, 405 (2011).

    Google Scholar 

  18. D. I. Lekhovytskiy, S. B. Milovanov, I. D. Rakov, B. G. Sverdlov, “Universal adaptive lattice filters. Adaptation for a given root of the estimating correlation matrix,” Radiophys. Quantum Electron. 35, No. 11–12, 621 (1992). DOI: 10.1007/BF01046658.

    Article  Google Scholar 

  19. D. I. Lekhovytskiy, “Thirty years experience in development of adaptive lattice filters theory, techniques and testing in Kharkiv,” Proc. of 2011 VIII Int. Conf. on Antenna Theory and Techniques, ICATT, 20–23 Sept. 2011, Kyiv, Ukraine (IEEE, 2011). DOI: 10.1109/ICATT.2011.6170713.

    Google Scholar 

  20. R. Nitzberg, “Application of maximum likelihood estimation of persymmetric covariance matrices to adaptive processing,” IEEE Trans. Aerosp. Electron. Syst. AES-16, No. 1, 124 (1980). DOI: 10.1109/TAES.1980.308887.

    Article  Google Scholar 

  21. D. I. Lekhovytskiy, “To the theory of adaptive signal processing in systems with centrally symmetric receive channels,” EURASIP J. Adv. Signal Process. 2016, No. 33 (2016). DOI: 10.1186/s13634-016-0329-z.

    Google Scholar 

  22. A. De Maio, D. Orlando, “An invariant approach to adaptive radar detection under covariance persymmetry,” IEEE Trans. Signal Process. 63, No. 5, 1297 (2015). DOI: 10.1109/TSP.2014.2388441.

    Article  MathSciNet  MATH  Google Scholar 

  23. Y. V. Shkvarko, “Unifying experiment design and convex regularization techniques for enhanced imaging with uncertain remote sensing data—Part I: Theory,” IEEE Trans. Geosci. Remote Sens. 48, No. 1, 82 (2010). DOI: 10.1109/TGRS.2009.2027695.

    Article  Google Scholar 

  24. Y. V. Shkvarko, “Unifying experiment design and convex regularization techniques for enhanced imaging with uncertain remote sensing data—Part II: Adaptive implementation and performance issues,” IEEE Trans. Geosci. Remote Sens. 48, No. 1, 96 (2010). DOI: 10.1109/TGRS.2009.2027696.

    Article  Google Scholar 

  25. Y. V. Shkvarko, J. Tuxpan, S. Santos, “Dynamic experiment design regularization approach to adaptive imaging with array radar/SAR sensor systems,” Sensors 11, No. 5, 4483 (2011). DOI: 10.3390/s110504483.

    Article  Google Scholar 

  26. G. D. Martín del Campo, A. Reigber, Y. V. Shkvarko, “Resolution enhanced SAR tomography: bold roman A nonparametric iterative adaptive approach,” in: Proc. of 2016 IEEE Int. Geoscience and Remote Sensing Symp., IGARSS, 10–15 Jul. 2016, Beijing, China (IEEE, 2016). DOI: 10.1109/IGARSS.2016.7729838.

    Google Scholar 

  27. D. S. Rachkov, D. I. Lekhovytskiy, “Lattice-filter-based unified structure of system for interperiod processing of weather radar signals,” in: Proc. of 2015 IEEE Radar Conf., RadarCon, 10–15 May 2015, Arlington, USA (IEEE, 2015), pp. 1234–1239. DOI: 10.1109/RADAR.2015.7131183.

    Chapter  Google Scholar 

  28. D. S. Rachkov, D. I. Lekhovytskiy, A. V. Semeniaka, B. M. Vovshin, U. U. Laurukevich, “Lattice implementation of ‘superresolving’ methods for meteorological objects spectra estimation,” in: Proc. of 2014 15th Int. Radar Symp., IRS, 16–18 Jun. 2014, Gdansk, Poland (IEEE, 2014), pp. 35–38. DOI: 10.1109/IRS. 2014.6869229.

    Google Scholar 

  29. K. C. Sharman, T. S. Durrani, “Spatial lattice filter for high-resolution spectral analysis of array data,” IEE Proc. F -Commun., Radar Signal Process. 130, No. 3, 279 (1983). DOI: 10.1049/ip-f-1:19830047.

    Article  Google Scholar 

  30. I. S. Reed, J. D. Mallett, L. E. Brennan, “Rapid convergence rate in adaptive arrays,” IEEE Trans. Aerosp. Electron. Syst. AES-10, No. 6, 853 (1974). DOI: 10.1109/TAES.1974.307893.

    Article  Google Scholar 

  31. D. S. Rachkov, D. I. Lekhovytskiy, A. V. Semeniaka, V. P. Riabukha, D. V. Atamanskiy, “Lattice-filter-based ground clutter canceller for pulse Doppler weather radar,” in: Proc. of 2014 15th Int. Radar Symp., IRS, 16–18 Jun. 2014, Gdansk, Poland (IEEE, 2014), pp. 215–219. DOI: 10.1109/IRS.2014.6869251.

    Google Scholar 

  32. D. I. Lekhovytskiy, D. V. Atamanskiy, V. P. Riabukha, D. S. Rachkov, A. V. Semeniaka, “Combining target detection against the background of jamming signals and jamming signal DOA estimation,” in: Proc. of 2015 Int. Conf. on Antenna Theory and Techniques, ICATT, 21–24 Apr. 2015, Kharkiv, Ukraine (IEEE, 2015), pp. 36–40. DOI: 10.1109/ICATT.2015.7136777.

    Google Scholar 

  33. W.-H. Yang, S. H. Holan, C. K. Wikle, “Bayesian lattice filters for time-varying autoregression and time-frequency analysis,” Bayesian Analysis 11, No. 4, 977 (2016). DOI: 10.1214/15-BA978.

    Article  MathSciNet  MATH  Google Scholar 

  34. M. T. Ozden, “Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification,” EURASIP J. Adv. Signal Process. 2018, 45 (2018). DOI: 10.1186/s13634-018-0567-3.

    Article  Google Scholar 

  35. A. Castillo-Atoche, D. Torres-Roman, Y. V. Shkvarko, “Towards real time implementation of reconstructive signal processing algorithms using systolic array coprocessors,” J. Syst. Archit. 56, No. 8, 327 (2010). DOI: 10.1016/j.sysarc.2010.05.004.

    Article  Google Scholar 

  36. Y. V. Shkvarko, J. I. Yanez, J. A. Amao, G. D. Martín del Campo, “Radar/SAR image resolution enhancement via unifying descriptive experiment design regularization and wavelet-domain processing,” IEEE Geosci. Remote Sens. Lett. 13, No. 2, 152 (2016). DOI: 10.1109/LGRS.2015.2502539.

    Article  Google Scholar 

  37. Y. I. Abramovich, N. K. Spencer, B. A. Johnson, “Band-inverse TVAR covariance matrix estimation for adaptive detection,” IEEE Trans. Aerosp. Electron. Syst. 46, No. 1, 375 (2010). DOI: 10.1109/TAES.2010. 5417169.

    Article  Google Scholar 

  38. D. I. Lekhovytskiy, Y. I. Abramovich, “Adaptive lattice filters for band-inverse (TVAR) covariance matrix approximations: theory and practical applications,” in: Proc. of 2009 Int. Radar Symp., IRS 2009, Hamburg, Germany (TUHH, Hamburg, 2009), pp. 535–539.

    Google Scholar 

  39. D. Lee, M. Morf, B. Friedlander, “Recursive least squares ladder estimation algorithms,” IEEE Trans. Acoust., Speech, Signal Process. 29, No. 3, 627 (1981). DOI: 10.1109/TASSP.1981.1163587.

    Article  MathSciNet  MATH  Google Scholar 

  40. H. Dym, I. Gohberg, “Extensions of band matrices with band inverses,” Linear Algebra and its Applications 36, 1 (1981). DOI: 10.1016/0024-3795(81)90215-9.

    Article  MathSciNet  MATH  Google Scholar 

  41. Y. I. Abramovich, N. K. Spencer, M. D. E. Turley, “Time-varying autoregressive (TVAR) models for multiple radar observations,” IEEE Trans. Signal Process. 55, No. 4, 1298 (2007). DOI: 10.1109/TSP.2006.888064.

    Article  MathSciNet  MATH  Google Scholar 

  42. Y. I. Abramovich, N. K. Spencer, M. D. E. Turley, “Order estimation and discrimination between stationary and time-varying (TVAR) autoregressive models,” IEEE Trans. Signal Process. 55, No. 6, 2861 (2007). DOI: 10.1109/TSP.2007.893966.

    Article  MathSciNet  MATH  Google Scholar 

  43. Y. I. Abramovich, B. A. Johnson, “Adaptive radar detection for sample-starved Gaussian training conditions,” in: A. De Maio, M. S. Greco [eds.], Modern Radar Detection Theory (SciTech Publishing, Edison, NJ, 2016), pp. 165–262. DOI: 10.1049/SBRA509E_ch6.

    Google Scholar 

  44. W. L. Melvin, “Space-time adaptive processing for radar,” in: N. D. Sidiropoulos, F. Gini, R. Chellappa, S. Theodoridis, (eds.), Academic Press Library in Signal Processing. Vol. 2: Communications and Radar Signal Processing (2014), Chapter 12, pp. 595–665. DOI: 10.1016/B978-0-12-396500-4.00012-0.

    Chapter  Google Scholar 

  45. Y. I. Abramovich, N. K. Spencer, A. Y. Gorokhov, “Modified GLRT and AMF framework for adaptive detectors,” IEEE Trans. Aerosp. Electron. Syst. 43, No. 3, 1017 (2007). DOI: 10.1109/TAES.2007.4383590.

    Article  Google Scholar 

  46. P. E. Gill, G. H. Golub, W. Murray, M. A. Saunders, “Methods for modifying matrix factorizations,” Math. Comp. 28, No. 126, 505 (1974). DOI: 10.1090/S0025-5718-1974-0343558-6.

    Article  MathSciNet  MATH  Google Scholar 

  47. K. D. Ikramov, Numerical Methods for Symmetric Linear Systems [in Russian] (Nauka, Moscow, 1988).

    MATH  Google Scholar 

  48. D. T. L. Lee, “Canonical ladder form realizations and fast estimation algorithms,” Ph.D. Dissertation (Stanford, CA, 1980).

    Google Scholar 

  49. O. Krause, C. Igel, “A more efficient rank-one covariance matrix update for evolution strategies,” in: Proc. of 2015 ACM Conf. on Foundations of Genetic Algorithms XIII, FOGA’15, 17–22 Jan. 2015, Aberystwyth, United Kingdom (ACM, New York, 2015), pp. 129–136. DOI: 10.1145/2725494.2725496.

    Chapter  Google Scholar 

  50. Z. Li, Q. Zhang, “An efficient rank-1 update for Cholesky CMA-ES using auxiliary evolution path,” in: Proc. of 2017 IEEE Congress on Evolutionary Computation, CEC, 5–8 Jun. 2017, San Sebastian, Spain (IEEE, 2017). DOI: 10.1109/CEC.2017.7969406.

    Google Scholar 

  51. Z. Li, Q. Zhang, “A simple yet efficient rank one update for covariance matrix adaptation,” arXiv preprint (2017). 16 p. URI: https://doi.org/arxiv.org/abs/1710.03996.

    Google Scholar 

  52. H.-G. Beyer, B. Sendhoff, “Simplify your covariance matrix adaptation evolution strategy,” IEEE Trans. Evol. Comput. 21, No. 5, 746 (2017). DOI: 10.1109/TEVC.2017.2680320.

    Article  Google Scholar 

  53. S. J. Olszanskyj, J. M. Lebak, A. W. Bojanczyk, “Rank-k modification methods for recursive least squares problems,” Numer. Algor. 7, No. 2, 325 (1994). DOI: 10.1007/BF02140689.

    Article  MathSciNet  MATH  Google Scholar 

  54. H. Oh, Z. Hu, “Multiple-rank modification of symmetric eigenvalue problem,” MethodsX 5, 103 (2018). DOI: 10.1016/j.mex.2018.01.001.

    Article  Google Scholar 

  55. L. Deng, “Multiple-rank updates to matrix factorizations for nonlinear analysis and circuit design,” Ph.D. Dissertation (Stanford, CA, 2010).

    Google Scholar 

  56. R. Bellman, Introduction to Matrix Analysis (McGraw-Hill, New York, 1960).

    MATH  Google Scholar 

  57. I. Stanimirovic, Computation of Generalized Matrix Inverses and Applications (Apple Academic Press, Waretown, NJ, 2017).

    Book  MATH  Google Scholar 

  58. C. M. Rader, A. O. Steinhardt, “Hyperbolic Householder transformations,” IEEE Trans. Acoust., Speech, Signal Process. 34, No. 6, 1589 (1986). DOI: 10.1109/TASSP.1986.1164998.

    Article  MATH  Google Scholar 

  59. A. W. Bojanczyk, A. O. Steinhardt, “Stabilized hyperbolic Householder transformations,” IEEE Trans. Acoust., Speech, Signal Process. 37, No. 8, 1286 (1989). DOI: 10.1109/29.31277.

    Article  MathSciNet  MATH  Google Scholar 

  60. A. W. Bojanczyk, J. G. Nagy, R. J. Plemmons, Row Householder transformations for rank-k Cholesky inverse modifications. IMA Preprint Series 978 (University of Minnesota, Minneapolis, MN, 1992). URI: https://doi.org/hdl.handle.net/11299/1897.

    Google Scholar 

  61. D. I. Lekhovytskiy, D. S. Rachkov, A. V. Semeniaka, “K-rank modification of adaptive lattice filter parameters,” in: Proc. of 2015 IEEE Radar Conf., RadarCon, 10–15 May 2015, Arlington, VA, USA (IEEE, 2015). DOI: 10.1109/RADAR.2015.7130983.

    Google Scholar 

  62. Y. I. Abramovich, “Controlled method for adaptive optimization of filters using the criterion of maximum signal-to-noise ratio,” Radiotekh. Elektron. 26, No. 3, 87 (1981).

    Google Scholar 

  63. H. V. Henderson, S. R. Searle, “On deriving the inverse of bold roman a sum of matrices,” SIAM Rev. 23, No. 1, 53 (1981). DOI: 10.1137/1023004.

    Article  MathSciNet  MATH  Google Scholar 

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Original Russian Text © D.I. Lekhovytskiy, 2018, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2018, Vol. 61, No. 11, pp. 607–644.

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Lekhovytskiy, D.I. Adaptive Lattice Filters for Systems of Space-Time Processing of Non-Stationary Gaussian Processes. Radioelectron.Commun.Syst. 61, 477–514 (2018). https://doi.org/10.3103/S0735272718110018

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