Maximum likelihood DOA estimator based on importance sampling

Jianguo Huang; Da Xie; Xiong Li; Qunfei Zhang

doi:10.1109/TENCON.2006.344051

Maximum likelihood DOA estimator based on importance sampling

Jianguo Huang, Da Xie, Xiong Li, Qunfei Zhang

School of Marine Science and Technology

Northwestern Polytechnical University Xian

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

1 Scopus citations

Abstract

DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.

Original language	English
Title of host publication	2006 IEEE Region 10 Conference, TENCON 2006
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	1424405491, 9781424405497
DOIs	https://doi.org/10.1109/TENCON.2006.344051
State	Published - 2006
Event	2006 IEEE Region 10 Conference, TENCON 2006 - Hong Kong, China Duration: 14 Nov 2006 → 17 Nov 2006

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2006 IEEE Region 10 Conference, TENCON 2006
Country/Territory	China
City	Hong Kong
Period	14/11/06 → 17/11/06

Access to Document

10.1109/TENCON.2006.344051

Cite this

@inproceedings{f2d087e980874dcbb576e73176c44dd5,

title = "Maximum likelihood DOA estimator based on importance sampling",

abstract = "DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.",

author = "Jianguo Huang and Da Xie and Xiong Li and Qunfei Zhang",

year = "2006",

doi = "10.1109/TENCON.2006.344051",

language = "英语",

isbn = "1424405491",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2006 IEEE Region 10 Conference, TENCON 2006",

note = "2006 IEEE Region 10 Conference, TENCON 2006 ; Conference date: 14-11-2006 Through 17-11-2006",

}

Huang, J, Xie, D, Li, X & Zhang, Q 2006, Maximum likelihood DOA estimator based on importance sampling. in 2006 IEEE Region 10 Conference, TENCON 2006., 4142204, IEEE Region 10 Annual International Conference, Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc., 2006 IEEE Region 10 Conference, TENCON 2006, Hong Kong, China, 14/11/06. https://doi.org/10.1109/TENCON.2006.344051

Maximum likelihood DOA estimator based on importance sampling. / Huang, Jianguo; Xie, Da; Li, Xiong et al.
2006 IEEE Region 10 Conference, TENCON 2006. Institute of Electrical and Electronics Engineers Inc., 2006. 4142204 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

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

TY - GEN

T1 - Maximum likelihood DOA estimator based on importance sampling

AU - Huang, Jianguo

AU - Xie, Da

AU - Li, Xiong

AU - Zhang, Qunfei

PY - 2006

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N2 - DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.

AB - DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.

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M3 - 会议稿件

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SN - 1424405491

SN - 9781424405497

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2006 IEEE Region 10 Conference, TENCON 2006

Y2 - 14 November 2006 through 17 November 2006

ER -

Maximum likelihood DOA estimator based on importance sampling

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