Frequency Diverse Coprime MIMO radar for Angle-Range Estimation
Pages 117 - 121
Abstract
Coprime arrays are able to utilize two subarrays to construct a difference coarray to obtain more degrees of freedom (DOF). A novel frequency diverse coprime array (FDCA) is developed for estimating the angle-range for FDA-multiple input multiple output (MIMO) radar. In addition, a tensor-based joint angle-range estimation algorithm is developed, which avoids the 2-dimensional spectral peak searching and substantially reduces the computational complexity. The FDCA is composed of two FDA subarrays, which requires only <Formula format="inline"><TexMath><?TeX $O\{ M + N\} $ ?></TexMath><File name="a00--inline1" type="gif"/></Formula> physical antennas to obtain <Formula format="inline"><TexMath><?TeX $O\{ MN\} $ ?></TexMath><File name="a00--inline2" type="gif"/></Formula> degrees of freedom (DOFs) compared to conventional FDA-MIMO radars. In this paper, we propose a tensor-based reduced-dimension multiple signal classification (RD-MUSIC) algorithm, which not only can obtain accurate parameter estimation in FDCA-MIMO radar, but also requires low computation. Simulation experiments verify the advantages of FDCA-MIMO radar and the developed algorithm.
References
[1]
Fishler E, Haimovich A, Blum R, MIMO radar: An idea whose time has come[C]//Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No. 04CH37509). IEEE, 2004: 71-78.
[2]
Wang X, Guo Y, Wen F, EMVS-MIMO radar with sparse Rx geometry: Tensor modeling and 2D direction finding[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023.
[3]
Guo Y, Wang X, Lan X, Traffic target location estimation based on tensor decomposition in intelligent transportation system[J]. IEEE Transactions on Intelligent Transportation Systems, 2022.
[4]
Antonik P, Wicks M C, Griffiths H D, Frequency diverse array radars[C]//2006 IEEE Conference on Radar. IEEE, 2006: 215-217.
[5]
Guo Y, Wang X, Shi J, Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error[J]. Remote Sensing, 2022, 14(6): 1405.
[6]
Xiong J, Wang W Q, Gao K. FDA-MIMO radar range–angle estimation: CRLB, MSE, and resolution analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 54(1): 284-294.
[7]
Feng M, Cui Z, Yang Y, A reduced-dimension MUSIC algorithm for monostatic FDA-MIMO radar[J]. IEEE Communications Letters, 2020, 25(4): 1279-1282.
[8]
Yan Y, Cai J, Wang W Q. Two-stage ESPRIT for unambiguous angle and range estimation in FDA-MIMO radar[J]. Digital Signal Processing, 2019, 92: 151-165.
[9]
Feng M, Yang Y, Shu Q, An improved ESPRIT-based algorithm for monostatic FDA-MIMO radar with linear or nonlinear frequency increments[J]. IEEE Communications Letters, 2021, 25(7): 2375-2379.
[10]
Vaidyanathan P P, Pal P. Sparse sensing with co-prime samplers and arrays[J]. IEEE Transactions on Signal Processing, 2010, 59(2): 573-586.
[11]
Qin S, Zhang Y D, Amin M G. Generalized coprime array configurations for direction-of-arrival estimation[J]. IEEE Transactions on Signal Processing, 2015, 63(6): 1377-1390.
[12]
Kolda T G, Bader B W. Tensor decompositions and applications[J]. SIAM review, 2009, 51(3): 455-500.
[13]
Wang X, Wang W, Liu J, Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling[J]. Signal Processing, 2015, 116: 152-158.
[14]
Zhang X, Xu L, Xu L, Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC[J]. IEEE communications letters, 2010, 14(12): 1161-1163.
[15]
Gui R, Wang W Q, Cui C, Coherent pulsed-FDA radar receiver design with time-variance consideration: SINR and CRB analysis[J]. IEEE Transactions on Signal Processing, 2017, 66(1): 200-214.
Index terms have been assigned to the content through auto-classification.
Recommendations
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In

April 2024
379 pages
ISBN:9798400709777
DOI:10.1145/3653644
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 September 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Natural Science Foundation of Hainan Province
- Key Research and Development Project of Hainan Province
- the National Natural Science Foundation of China
Conference
FAIML 2024
FAIML 2024: 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning
April 26 - 28, 2024
Yichang, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 15Total Downloads
- Downloads (Last 12 months)15
- Downloads (Last 6 weeks)5
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format