- MIMO Radar Optimization With Constant-Modulus and Any p-Norm Similarity Constraints He Xin(hx66hxgmail.com) Abstract: MIMO radar plays a key role in autonomous driving, and the similarity waveform constraint is an important constraint for radar waveform design. However, the joint constant-modulus and similarity constraint is a difficult constraint. Only the special case with $\infty$-norm similarity and constant-modulus constraints is tackled by the semidefinite relaxation (SDR) and the successive quadratic refinement (SQR) methods. In this paper, the joint constant-modulus and any p-norm (1<= p<=$\infty$) similarity constraint is tackled by the proposed relax-and-retract algorithm. In particular, the nonconvex constant-modulus constraint is first relaxed to convex constraint, and then the retract operation is guaranteed to recover a constant-modulus solution within a fixed iteration number. Extensive simulation results show that full range similarity control and constant-modulus constraints are satisfied under different $p$-norms. For the special case with $1$-norm, it is firstly found to be a constant-modulus-inducing norm. For the special case with $\infty$-norm, the proposed relax-and-retract method has less computational time than the SDR and SQR methods. Keywords: MIMO Radar, Constant-Modulus, Unit-Modulus, $p$-Norm, Similarity Constraint, Relax-and-Retract. Category 1: Linear, Cone and Semidefinite Programming (Second-Order Cone Programming ) Category 2: Convex and Nonsmooth Optimization (Convex Optimization ) Category 3: Applications -- OR and Management Sciences (Telecommunications ) Citation: Shenzhen University, Hanshan Normal University, China. May/2021. Download: [PDF]Entry Submitted: 05/22/2021Entry Accepted: 05/28/2021Entry Last Modified: 05/22/2021Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.