Unconditional maximum likelihood approach for localization of near-field sources in 3-D space

Nihat Kabaog̃lu; Hakan A. Çirpan; Selçuk Paker

Unconditional maximum likelihood approach for localization of near-field sources in 3-D space

Nihat Kabaog̃lu^*, Hakan A. Çirpan, Selçuk Paker

^*Corresponding author for this work

Department of Electronics and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios, we propose unconditional ML (UML) method for estimating azimuth, elevation and range parameters of near-field sources in 3-D space in this paper. Besides these superiorities, stability, asymptotic unbiasedness, asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages, ML estimator has computational complexity. Fortunately, this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.

Original language	English
Title of host publication	Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology
Pages	233-237
Number of pages	5
Publication status	Published - 2004
Event	Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004 - Rome, Italy Duration: 18 Dec 2004 → 21 Dec 2004

Publication series

Name	Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004

Conference

Conference	Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004
Country/Territory	Italy
City	Rome
Period	18/12/04 → 21/12/04

Cite this

Kabaog̃lu, N., Çirpan, H. A., & Paker, S. (2004). Unconditional maximum likelihood approach for localization of near-field sources in 3-D space. In Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology (pp. 233-237). (Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004).

Kabaog̃lu, Nihat ; Çirpan, Hakan A. ; Paker, Selçuk. / Unconditional maximum likelihood approach for localization of near-field sources in 3-D space. Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology. 2004. pp. 233-237 (Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004).

@inproceedings{a59150bfbc9242129de197863c134dab,

title = "Unconditional maximum likelihood approach for localization of near-field sources in 3-D space",

abstract = "Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios, we propose unconditional ML (UML) method for estimating azimuth, elevation and range parameters of near-field sources in 3-D space in this paper. Besides these superiorities, stability, asymptotic unbiasedness, asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages, ML estimator has computational complexity. Fortunately, this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.",

author = "Nihat Kabao{\~g}lu and {\c C}irpan, {Hakan A.} and Sel{\c c}uk Paker",

year = "2004",

language = "English",

isbn = "0780386892",

series = "Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004",

pages = "233--237",

booktitle = "Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology",

note = "Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004 ; Conference date: 18-12-2004 Through 21-12-2004",

}

Kabaog̃lu, N, Çirpan, HA & Paker, S 2004, Unconditional maximum likelihood approach for localization of near-field sources in 3-D space. in Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology. Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004, pp. 233-237, Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004, Rome, Italy, 18/12/04.

Unconditional maximum likelihood approach for localization of near-field sources in 3-D space. / Kabaog̃lu, Nihat; Çirpan, Hakan A.; Paker, Selçuk.
Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology. 2004. p. 233-237 (Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Unconditional maximum likelihood approach for localization of near-field sources in 3-D space

AU - Kabaog̃lu, Nihat

AU - Çirpan, Hakan A.

AU - Paker, Selçuk

PY - 2004

Y1 - 2004

N2 - Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios, we propose unconditional ML (UML) method for estimating azimuth, elevation and range parameters of near-field sources in 3-D space in this paper. Besides these superiorities, stability, asymptotic unbiasedness, asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages, ML estimator has computational complexity. Fortunately, this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.

AB - Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios, we propose unconditional ML (UML) method for estimating azimuth, elevation and range parameters of near-field sources in 3-D space in this paper. Besides these superiorities, stability, asymptotic unbiasedness, asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages, ML estimator has computational complexity. Fortunately, this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.

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M3 - Conference contribution

AN - SCOPUS:21544453326

SN - 0780386892

T3 - Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004

SP - 233

EP - 237

BT - Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology

T2 - Fourth IEEE International Symposium on Signal processing and Information Technology, ISSPIT 2004

Y2 - 18 December 2004 through 21 December 2004

ER -

Kabaog̃lu N, Çirpan HA, Paker S. Unconditional maximum likelihood approach for localization of near-field sources in 3-D space. In Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology. 2004. p. 233-237. (Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2004).

Unconditional maximum likelihood approach for localization of near-field sources in 3-D space

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