TY - JOUR
T1 - MIMO Radar Waveform Design in the Presence of Multiple Targets and Practical Constraints
AU - Yu, Xianxiang
AU - Alhujaili, Khaled
AU - Cui, Guolong
AU - Monga, Vishal
N1 - Funding Information:
Manuscript received July 15, 2019; revised January 22, 2020; accepted February 27, 2020. Date of publication March 12, 2020; date of current version April 6, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Fabiola Colone. This work was supported in part by the National Natural Science Foundation of China under Grants 61771109, 61871080, and 61701088, in part by ChangJiang Scholar Program, in part by 111 Project B17008, in part by Fundamental Research Funds for the Central Universities under Grant 2672018ZYGX2018J016, and in part by China Scholarship Council and University of Electronic Science and Technology of China. (Corresponding author: Guolong Cui.) Xianxiang Yu and Guolong Cui are with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xianxiangy@gmail.com; cuiguolong@uestc.edu.cn).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - This paper deals with the joint design of Multiple-Input Multiple-Output (MIMO) radar transmit waveform and receive filter to enhance multiple targets detectability in the presence of signal-dependent (clutter) and independent disturbance. The worst-case Signal-to-Interference-Noise-Ratio (SINR) over multiple targets is explicitly maximized. To ensure hardware compatibility and the coexistence between MIMO radar and other wireless systems, constant modulus and spectral restrictions on the waveform are incorporated in our design. A max-min non-convex optimization problem emerges as a function of the transmit waveform, which we solve via a novel polynomial-time iterative procedure that involves solving a sequence of convex problems with constraints that evolve with every iteration. The overall algorithm follows an alternate optimization over the receive filter and transmit waveform. For the problem of waveform optimization (which is our central contribution), we provide analytical guarantees of monotonic cost function improvement with proof of convergence to a solution that satisfies the KarushKuhnTucker (KKT) conditions. We also develop extensions that address the well-known waveform similarity constraint. By simulating challenging practical scenarios, we evaluate the proposed algorithm against the state-of-the-art methods in terms of the achieved SINR value and the computational complexity. Overall, we show that our proposal outperforms state of the art competing methods while providing the most favorable performance-complexity balance.
AB - This paper deals with the joint design of Multiple-Input Multiple-Output (MIMO) radar transmit waveform and receive filter to enhance multiple targets detectability in the presence of signal-dependent (clutter) and independent disturbance. The worst-case Signal-to-Interference-Noise-Ratio (SINR) over multiple targets is explicitly maximized. To ensure hardware compatibility and the coexistence between MIMO radar and other wireless systems, constant modulus and spectral restrictions on the waveform are incorporated in our design. A max-min non-convex optimization problem emerges as a function of the transmit waveform, which we solve via a novel polynomial-time iterative procedure that involves solving a sequence of convex problems with constraints that evolve with every iteration. The overall algorithm follows an alternate optimization over the receive filter and transmit waveform. For the problem of waveform optimization (which is our central contribution), we provide analytical guarantees of monotonic cost function improvement with proof of convergence to a solution that satisfies the KarushKuhnTucker (KKT) conditions. We also develop extensions that address the well-known waveform similarity constraint. By simulating challenging practical scenarios, we evaluate the proposed algorithm against the state-of-the-art methods in terms of the achieved SINR value and the computational complexity. Overall, we show that our proposal outperforms state of the art competing methods while providing the most favorable performance-complexity balance.
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U2 - 10.1109/TSP.2020.2979602
DO - 10.1109/TSP.2020.2979602
M3 - Article
AN - SCOPUS:85083327408
SN - 1053-587X
VL - 68
SP - 1974
EP - 1989
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
M1 - 9034082
ER -