MIMO Radar Waveform Design for Range-ISL Optimization via Iterative Deep Unfolding Network
Ziwei Zhao, Jinfeng Hu, Kai Zhong, Yongfeng Zuo, Huiyong Li, Bozhou Zhang
Abstract
Multiple Input Multiple Output (MIMO) radar unimodular waveform design with range-ISL optimization is a key technology in remote sensing. Due to the non-convex quartic objective function and constant modulus constraint (CMC), the problem is NP-hard and non-convex. Existing methods mainly include relaxation methods or non-relaxation methods with huge computational cost. We notice that complex circle manifold (CCM) naturally satisfies the CMC. By projecting onto the CCM, the problem is transformed into an unconstrained minimization problem that can be addressed using the Riemannian gradient descent (RGD) algorithm. Furthermore, we notice that the RGD algorithm can be unfolded into a deep learning model. Hence, a computationally efficient method without relaxation, Iterative Deep Unfolding Network (IDUN), is proposed. First, this problem is converted into an unconstrained fourth-order polynomial minimization problem on the CCM. Then, by unfolding RGD algorithm as the network layer, IDUN is developed with adaptively learning the step sizes. Compared with existing methods, the proposed method has superior performance and less computational cost.