TY - JOUR
T1 - Manifold optimization for joint design of MIMO-STAP radars
AU - Li, Jie
AU - Liao, Guisheng
AU - Huang, Yan
AU - Nehorai, Arye
N1 - Funding Information:
Manuscript received June 28, 2020; revised August 18, 2020; accepted August 31, 2020. Date of publication October 28, 2020; date of current version November 13, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61621005, in part by the National Natural Science Foundation of China under Grant 61901112, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20190330, in part by the Advanced Research Foundation under Grant 61404130223, and in part by the Fundamental Research for the Central Universities under Grant 1104000399. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ioannis D. Schizas. (Corresponding authors: Jie Li; Guisheng Liao; Yan Huang.) Jie Li and Guisheng Liao are with the National Key Laboratory of Radar Signal Processing, Xidian University, X’ian 710071, China (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - In order to maximize the signal-to-interference-plusnoise ratio (SINR) under a constant-envelope (CE) constraint, a fast and efficient joint design of the transmit waveform and the receive filter for colocated multiple-input multiple-output (MIMO) radars is essential. Conventional joint optimization is performed using nonlinear optimization techniques such as the semidefinite relaxation (SDR) algorithm. In this letter, we propose a novel manifold-based alternating optimization (MAO) method, which reformulates the waveform optimization subproblem as an unconstrained optimization problem on a Riemannian manifold. We present the geometrical structure of the feasible region and derive the explicit expressions for the Riemannian gradient and the Riemannian Hessian, thus the reformulated optimization could be solved by using the Riemannian trust-region (RTR) algorithm. Numerical experiments demonstrate that the proposedmethod has faster convergencewith reduced computational cost comparedwith conventional SDR-based algorithm in Euclidean space.
AB - In order to maximize the signal-to-interference-plusnoise ratio (SINR) under a constant-envelope (CE) constraint, a fast and efficient joint design of the transmit waveform and the receive filter for colocated multiple-input multiple-output (MIMO) radars is essential. Conventional joint optimization is performed using nonlinear optimization techniques such as the semidefinite relaxation (SDR) algorithm. In this letter, we propose a novel manifold-based alternating optimization (MAO) method, which reformulates the waveform optimization subproblem as an unconstrained optimization problem on a Riemannian manifold. We present the geometrical structure of the feasible region and derive the explicit expressions for the Riemannian gradient and the Riemannian Hessian, thus the reformulated optimization could be solved by using the Riemannian trust-region (RTR) algorithm. Numerical experiments demonstrate that the proposedmethod has faster convergencewith reduced computational cost comparedwith conventional SDR-based algorithm in Euclidean space.
KW - Manifold optimization
KW - Multiple-input multiple-output (MIMO) radar
KW - Receive filter design
KW - Waveform design
UR - http://www.scopus.com/inward/record.url?scp=85092720787&partnerID=8YFLogxK
U2 - 10.1109/LSP.2020.3022239
DO - 10.1109/LSP.2020.3022239
M3 - Article
AN - SCOPUS:85092720787
SN - 1070-9908
VL - 27
SP - 1969
EP - 1973
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
M1 - 9242300
ER -