TY - JOUR
T1 - SUMMeR
T2 - Sub-Nyquist MIMO Radar
AU - Cohen, Deborah
AU - Cohen, David
AU - Eldar, Yonina C.
AU - Haimovich, Alexander M.
N1 - Funding Information:
This work was supported by the European Union?s Horizon 2020 research and innovation program under Grant 646804-ERC-COG-BNYQ, and in part by the Israel Science Foundation under Grant 335/14.
Funding Information:
Manuscript received December 21, 2016; revised December 18, 2017; accepted April 14, 2018. Date of publication May 21, 2018; date of current version July 10, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Amir Asif. This work was supported by the European Union’s Horizon 2020 research and innovation program under Grant 646804-ERC-COG-BNYQ, and in part by the Israel Science Foundation under Grant 335/14. The work of Deborah Cohen was supported by the Azrieli Fellowship from Azrieli Foundation. (Corresponding author: Deborah Cohen.) David Cohen, Deborah Cohen, and Y. C. Eldar are with the Electrical Engineering Department, Technion, Haifa 3200003, Israel (e-mail:,davidco@tech nion.ac.il; deborah.co88@gmail.com; yonina@ee.technion.ac.il).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - Multiple-input multiple-output (MIMO) radar exhibits several advantages with respect to the traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this paper, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets' parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas' locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.
AB - Multiple-input multiple-output (MIMO) radar exhibits several advantages with respect to the traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this paper, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets' parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas' locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.
KW - Compressed sensing
KW - MIMO radar
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U2 - 10.1109/TSP.2018.2838541
DO - 10.1109/TSP.2018.2838541
M3 - Article
AN - SCOPUS:85047191998
SN - 1053-587X
VL - 66
SP - 4315
EP - 4330
JO - IRE Transactions on Audio
JF - IRE Transactions on Audio
IS - 16
ER -