Fast Parameter Estimation Algorithms for Conformal FDA-MIMO Radar

In order to avoid the higher computational complexity caused by multidimensional parameter search, we investigate three reduced-dimension parameter estimation algorithms for the conformal frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, which are named the reduced-dimension m...

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Vydané v:IEEE sensors journal Ročník 23; číslo 24; s. 1
Hlavní autori: Tian, Xiang, Chen, Hui, Huang, Bang, Wang, Wen-Qin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 15.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive algorithms
Adaptive systems
Algorithms
Classification algorithms
Complexity
Conformal array
Covariance matrices
frequency diverse array multiple-input multiple-output (FDA-MIMO) radar
Frequency diversity
Frequency estimation
Manganese
MIMO communication
Numerical methods
Parameter estimation
Radar arrays
reduced-dimension
Sensors
Signal classification
sub-array
Synthetic aperture radar
ISSN:1530-437X, 1558-1748
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Shrnutí:In order to avoid the higher computational complexity caused by multidimensional parameter search, we investigate three reduced-dimension parameter estimation algorithms for the conformal frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, which are named the reduced-dimension multiple signal classification (RD-MUSIC), RD-MUSIC based on sub-array (RDMBS), and parameter separation and estimation based on virtual sub-array (PSEBVS), respectively. All proposed methods are verified by numerical results which show that the proposed algorithms can make a balance between complexity and precision, such as the PSEBVS achieves lower complexity with coarse precision, while the RDMBS algorithm benefits better estimation performance at the cost of a little higher complexity. Besides, compared with three-dimensional multiple signal classification (3D-MUISC) algorithm, all the proposed algorithms can jointly estimate the angle and range with lower complexity as well as the comparable estimation performance, especially for RD-MUSIC and RDMBS algorithms. Finally, an adaptive selection system for parameter estimation algorithms is presented to satisfy various requirements in different application scenarios.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3325452