Underdetermined direction-of-departure and direction-of-arrival estimation in bistatic multiple-input multiple-output radar
被引:13
作者:
Chan, Frankie K. W.
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机构:
City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
Chan, Frankie K. W.
[1
]
So, H. C.
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机构:
City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
So, H. C.
[1
]
Huang, Lei
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机构:
Harbin Inst Technol, Shenzhen Grad Sch, Dept Elect & Informat Engn, Shenzhen, Peoples R ChinaCity Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
Huang, Lei
[2
]
Huang, Long-Ting
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h-index: 0
机构:
City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
Huang, Long-Ting
[1
]
机构:
[1] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China
[2] Harbin Inst Technol, Shenzhen Grad Sch, Dept Elect & Informat Engn, Shenzhen, Peoples R China
Direction-of-arrival estimation;
Direction-of-departure estimation;
Subspace method;
Maximum likelihood estimator;
Alternating optimization;
MIMO RADAR;
DOA ESTIMATION;
D O I:
10.1016/j.sigpro.2014.04.019
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
In this paper, target localization using bistatic multiple-input multiple-output radar where the source number exceeds the sizes of the transmit and receive arrays, denoted by M and N, respectively, is addressed. We consider the Swerling II target in which the radar cross section varies in different pulses. Two algorithms for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of the targets are devised. The first one is a subspace-based estimator which is computationally simpler and can identify up to 2(M - 1) (2N - 1) sources, assuming that N >= M. The second is a maximum likelihood method with a higher estimation accuracy, where the DODs and DOAs are solved via alternating optimization. Simulation results are included to compare their mean square error performance with the Cramer-Rao lower bound. (C) 2014 Published by Elsevier B.V.