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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2009 / Cilt: 9 - Sayı: 1

Near field localization of moving sources with REM algorithms

Nihat KABAOĞLU
Serap ÇEKLİ
Erdinç ÇEKLİ
Pages
769–779
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This paper concludes by presenting the simulation results of the suggested algorithms in order to illustrate the computational effectiveness of the both algorithms.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we present the problem of joint tracking of the direction of arrival (DOA) and range parameters of moving sources in the near field of an antenna array with two Expectation- Maximization (EM) based recursive algorithms.
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Key Findings & Data:The main characteristic of the first Recursive EM (REM) approach is to include computation of the gradient of the log-likelihood function and some form of the complete data Fisher information matrix.
💡
Conclusion & Impact:This paper concludes by presenting the simulation results of the suggested algorithms in order to illustrate the computational effectiveness of the both algorithms.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, we present the problem of joint tracking of the direction of arrival (DOA) and range parameters of moving sources in the near field of an antenna array with two Expectation- Maximization (EM) based recursive algorithms. The main characteristic of the first Recursive EM (REM) approach is to include computation of the gradient of the log-likelihood function and some form of the complete data Fisher information matrix. Compared to first REM approach, the second one utilizes the stochastic approximation of approximate conditional expectation of the complete data sufficient statistics. The proposed recursive algorithms in this work assume that the parameters of interest are described by a linear polynomial model. This paper concludes by presenting the simulation results of the suggested algorithms in order to illustrate the computational effectiveness of the both algorithms.

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Cite This Article

KABAOĞLU, N., ÇEKLİ, S., ÇEKLİ, E. (2022). Near field localization of moving sources with REM algorithms. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 9 - Sayı: 1, 769–779. https://ajindex.com/articles/543755

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References (13)

  1. H. Jiang, S.X. Wang, H.J. Lu, “A Novel Higher Order Cyclic Cumulant Based Direction Finding Algorithm In Multipath” Signal Processing, Procs. ICSP '04. 7th Int. Conf., Vol: 1, pp. 411-414, 31 Aug.-4 Sept. 2004
  2. M.J.D. Rendas, J.M.F. Moura, “Path Resolution By Coherent Averaging: Trading Spatial And Temporal Degrees Of Freedom”, ICASSP-88, Int. Conf. Acou., Speech, Sig.Proc., Vol: 5, pp. 2626-2629, 11-14 April 1988
  3. N. Kabaoğlu, H.A. Çırpan, S. Paker, “Unconditional Maximum Likelihood Approach For Localization Of Near-Field Sources In 3D Space”, Proce. of the Fourth IEEE International Symp. on Sig. Processing and Inf. Tech., pp. 233- 237, 18-21 Dec., 2004
  4. A.P. Dempster, N. Laird, D.B. Rubin, “Maximum Likelihood From Incomplete Data Via The EM Algorithm”, J. Roy. Statist. Soc. Ser. B, Vol: 39, No. 1, pp. 1-38, 1977
  5. D.M. Titterington, “Recursive Parameter Estimation Using Incomplete Data” J. Roy. Stat. Soc. Ser. B, Vol: 46, No. 2, pp. 257-267, 1984
  6. M.I. Miller, D.R. Fuhrmann, “Maximum- Likelihood Narrow-Band Direction Finding And The EM Algorithm”, IEEE Trans. Acou. Speech, Sig. Proc., Vol: 38, pp. 1560-1577, Sept. 1990
  7. E. Çekli, H.A. Çırpan, “Unconditional Maximum Likelihood Approach for Localization of Near-Field Sources: Algorithm and Performance Analysis”, AEÜ Int. Journal Elect. and Comm., Vol. 57, No. 1, pp. 9-15, 2003
  8. N. Kabaoglu, H.A. Cirpan, S. Paker, “EM Based Stochastic Maximum Likelihood Approach for Localization of Near-Field Sources in 3-D,” Frequenz, Journal Telecommunications, Vol. 58, pp. 178-184, 7-8/2004
  9. J.A. Olague, D.C. Rosales, J.L. Rivera, “Efficiency Evaluation Of The Unconditional Maximum Likelihood Estimator For Near-Field DOA Estimation” ETRI Journal, Vol: 28, No. 6, pp. 761-769, Dec. 2006
  10. L. Frenkel, M. Feder, “Recursive Expectation-Maximization (EM) Algorithms For Time Varying Parameters With Applications To Multiple Target Tracking”, IEEE Trans. Signal Proc., Vol: 47, No. 2, pp. 306-320, Feb. 1999
  11. P. J. Chung, J. F. Böhme, A. O. Hero, “Tracking Of Multiple Moving Sources Using Recursive EM Algorithm”, EURASIP Journal on Applied Signal Proces., No.1, 50-60., 2005 [12] O. Cappe, M. Charbit, E. Moulines, “Recursive EM Algorithm With Applications To DOA Estimation”, ICASSP Proc. IEEE Int. Conf. Acoustics, Speech Signal Proc., 2006
  12. G.E. Forsythe, M.A. Malcolm, C.B. Moler, Computer Methods For Mathematical Computations, Prentice Hall, 1976
  13. A. Benveniste, M. M´etivier, P.