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Clutter Parameter Estimation Based on Indirect Algorithms

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Abstract

Indirect algorithms for clutter parameter estimation using linear transformations of initial data have been synthesized by the maximum likelihood method. The likelihood function was introduced for the input samples transformed in accordance with the sum-difference algorithms. The estimation algorithms of the interperiod correlation and Doppler phase shift coefficients derived by solving the appropriate likelihood equations do not contain the traditional complex multiplication operation over input data. The functional block diagram of the estimator of appropriate clutter parameters that can be used in adaptive rejection filters is presented. The estimation accuracy analysis of required parameters of clutter is performed depending on the size of training sample and correlation properties of clutter. The comparison of simulation and calculation results for indirect and direct algorithms revealed their complete match and confirmed the asymptotic efficiency of resultant estimates and the equivalence of indirect and direct algorithms.

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References

  1. M. I. Skolnik (ed.), Radar Handbook, 3rd ed. (McGraw–Hill, 2008).

    Google Scholar 

  2. M. A. Richards, J. A. Scheer, and W. A. Holm (eds.), Principles of Modern Radar: Basic Principles (SciTech Pub., IET, Edison, New York, 2010).

    Book  Google Scholar 

  3. D. I. Popov, “An adaptation of nonrecursive rejectors,” Radioelectron. Commun. Syst. 52, No. 4, 197 (2009). DOI: https://doi.org/10.3103/S0735272709040050.

    Article  Google Scholar 

  4. I. F. Lozovskyi, “Construction and efficiency of adaptive signal processing in conditions of multiple noises,” Uspekhi Sovremennoi Radioelektroniki. Zarubezhnaya Radioelektronika, No. 1, 52 (2016). URI: http://www. radiotec.ru/article/7230#english.

    Google Scholar 

  5. W. L. Melvin, J. A. Scheer (eds.), Principles of Modern Radar: Advanced Techniques (SciTech Publishing, IET, Edison, New York, 2013).

    Google Scholar 

  6. M. A. Richards, Fundamentals of Radar Signal Processing, 2nd ed. (McGraw–Hill Education, New York, 2014).

    Google Scholar 

  7. D. I. Popov, S. M. Smolskiy, “Estimation of the clutter correlation coefficient in radar systems,” Infocommun. J. VIII, No. 3, 8 (Sept. 2016).

    Google Scholar 

  8. G. Perozzi, D. Efimov, J.–M. Biannic, L. Planckaert, P. Coton, “Wind estimation algorithm for quadrotors using detailed aerodynamic coefficients,” Proc. of Annual American Control Conf., ACC, 27–29 Jun. 2018, Milwaukee, USA (IEEE, 2018), pp. 1921–1926. DOI: https://doi.org/10.23919/ACC.2018.8431879.

    Google Scholar 

  9. E. Malikov, Y. Sun, “Semiparametric estimation and testing of smooth coefficient spatial autoregressive models,” J. Econometrics 199, No. 1, 12 (2017). DOI: https://doi.org/10.1016/j.jeconom.2017.02.005.

    Article  MathSciNet  MATH  Google Scholar 

  10. S. Tarima, “Statistical estimation in the presence of possibly incorrect model assumptions,” J. Statistical Theory Practice 11, No. 3, 449 (2017). DOI: https://doi.org/10.1080/15598608.2017.1299056.

    Article  MathSciNet  Google Scholar 

  11. B. Karmakar, I. Mukhopadhyay, “Risk efficient estimation of fully dependent random coefficient autoregressive models of general order,” Commun. Statistics–Theory Methods 47, No. 17, 4242 (2018). DOI: https://doi.org/10.1080/03610926.2017.1371758.

    Article  MathSciNet  Google Scholar 

  12. Y. Sun, E. Malikov, “Estimation and inference in functional–coefficient spatial autoregressive panel data models with fixed effects,” J. Econometrics 203, No. 2, 359 (2018). DOI: https://doi.org/10.1016/j.jeconom.2017.12.006.

    Article  MathSciNet  MATH  Google Scholar 

  13. V. M. Koshevoi and A. D. Medvedik, “Development of estimators of clutter parameters based on circuits of alternate period compensation,” Radioelectron. Commun. Syst. 23, No. 12, 82 (1980).

    Google Scholar 

  14. C. J. M. Olguin, S. C. Sampaio, R. R. dos Reis, “Statistical equivalence of prediction models of the soil sorption coefficient obtained using different log P algorithms,” Chemosphere 184, 498 (2017). DOI: https://doi.org/10.1016/j.chemosphere.2017.06.027.

    Article  Google Scholar 

  15. A. Pepelyshev, V. Kornikov, A. Zhigljavsky, “Statistical estimation in global random search algorithms in case of large dimensions,” in: R. Battiti, D. Kvasov, Y. Sergeyev (eds.), Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science, Vol. 10556 (Cham, Springer, 2017). DOI: https://doi.org/10.1007/978-3-319-69404-7_32.

  16. X. Hua, Y. Cheng, H. Wang, Y. Qin, “Information geometry for covariance estimation in heterogeneous clutter with total Bregman divergence,” Entropy 20, No. 4, 258 (2018). DOI: https://doi.org/10.3390/e20040258.

    Article  Google Scholar 

  17. N. Lazrieva, T. Toronjadze, “Recursive estimation procedures for one–dimensional parameter of statistical models associated with semimartingales,” Trans. A. Razmadze Math. Institute 171, No. 1, 57 (2017). DOI: https://doi.org/10.1016/j.trmi.2016.12.001.

    Article  MathSciNet  Google Scholar 

  18. D. I. Popov, “Adaptive clutter rejection,” Radioelectron. Commun. Syst. 56, No. 4, 186 (2013). DOI: https://doi.org/10.3103/S0735272713040031.

    Article  Google Scholar 

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Correspondence to D. I. Popov.

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Russian Text © D.I. Popov, 2019, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2019, Vol. 62, No. 1, pp. 54–63.

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Popov, D.I. Clutter Parameter Estimation Based on Indirect Algorithms. Radioelectron.Commun.Syst. 62, 42–50 (2019). https://doi.org/10.3103/S0735272719010060

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